{
 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "<!--BOOK_INFORMATION-->\n",
    "<a href=\"https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv\" target=\"_blank\"><img align=\"left\" src=\"data/cover.jpg\" style=\"width: 76px; height: 100px; background: white; padding: 1px; border: 1px solid black; margin-right:10px;\"></a>\n",
    "*This notebook contains an excerpt from the book [Machine Learning for OpenCV](https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv) by Michael Beyeler.\n",
    "The code is released under the [MIT license](https://opensource.org/licenses/MIT),\n",
    "and is available on [GitHub](https://github.com/mbeyeler/opencv-machine-learning).*\n",
    "\n",
    "*Note that this excerpt contains only the raw code - the book is rich with additional explanations and illustrations.\n",
    "If you find this content useful, please consider supporting the work by\n",
    "[buying the book](https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv)!*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Understanding Ensemble Methods](10.01-Understanding-Ensemble-Methods.ipynb) | [Contents](../README.md) | [Using Random Forests for Face Recognition](10.03-Using-Random-Forests-for-Face-Recognition.ipynb) >"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Combining Decision Trees Into a Random Forest\n",
    "\n",
    "A popular variation of bagged decision trees are the so-called **random forests**. These are\n",
    "essentially a collection of decision trees, where each tree is slightly different from the others.\n",
    "In contrast to bagged decision trees, each tree in a random forest is trained on a slightly\n",
    "different subset of data features.\n",
    "\n",
    "Although a single tree of unlimited depth might do a relatively good job of predicting the\n",
    "data, it is also prone to overfitting. The idea behind random forests is to build a large\n",
    "number of trees, each of them trained on a random subset of data samples and features.\n",
    "Because of the randomness of the procedure, each tree in the forest will overfit the data in a\n",
    "slightly different way. The effect of overfitting can then be reduced by averaging the\n",
    "predictions of the individual trees."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Understanding the shortcomings of decision trees\n",
    "\n",
    "The effect of overfitting the dataset, which a decision tree often falls victim of is best\n",
    "demonstrated through a simple example.\n",
    "\n",
    "For this, we will return to the `make_moons` function from scikit-learn's datasets module,\n",
    "which we previously used in [Chapter 8](08.00-Discovering-Hidden-Structures-with-Unsupervised-Learning.ipynb), *Discovering Hidden Structures with Unsupervised\n",
    "Learning* to organize data into two interleaving half circles. Here, we choose to generate 100\n",
    "data samples belonging to two half circles, in combination with some Gaussian noise with\n",
    "standard deviation 0.25:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "X, y = make_moons(n_samples=100, noise=0.25, random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
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MiEJcnAR33RWGjRtH4ZNPhiI42L1Nj9vRKBRabNqUh4EDN+Odd07g7bfvRlycpFWbwYMj\n8NNPExAf7wWBgIMlS/ohLMzwaOjw4ZEYOTLSHqETQm5Tu1pjJ5PJ4Ot7o1irVCqFTCaDRCIxcS9C\nSHS0+ffI/fcnwNfX9IiMWm1+jdW/y2XcKqmUj7vuCkK/foFQKrVwd+dalJiSG44cKcELL/wGADh/\nXobly49j0qQETJmSDK1Whx49gtC9uy+EwhubZaKjPfH99+Nx6FARPvroDGQyJeLiJHjqqe7o1SsQ\nfn40DUtIe9CuEjtD6CxJQsyLjRVj/Pg4bN+eZ/A6j8fB1KlJ4JrYFMuyLEaMiDI7ajdqVDR0lpw6\nbwaPx0AsbvcfUXZXVaXCa68dbXWbQqHB119nt/w7JcUX338/rlViBwDh4SLMnJmIsWNjoFZrIRLx\nIBLR74CQ9qRdvWOlUimqqm7UZKqqqoKPj4/BtllZWcjKymr5d1paGsTijrdNn8/nU787EGP99vRk\n8cYbd0Eub8KBAwWtrgmFXHzxxWj06hUKHs90uZO+fUMhErlBLm8yeF0o5OLOO8Pt/rO3x++bZVlU\nVSlRUtK8XjEoSAR/f5FDv1wa6ndmZo3R3c/XZWVVoaxMhdBQqcHrzv7Wofd3x9JR+w0AW7Zsafn/\nlJQUpKSkmL2P0yV2LMuCNVIOv2fPnti7dy/69++PnJwciEQio9Owhn4A9fXmF5C7GrFY7HL9rq5W\no6ZGDR6PQWCgO/h8/fVcrthvS5jqt78/Dx98MBh5ebU4dqwYtbUqdOnij9RUf0REeEKpNL+bNTRU\ngK+/HoMZM3ZAodC0uiYUcvH112MREeFu95+9rX/fcrkGf/xxDf/73wlkZTV/uUxI8MGiRb1x550h\nJkcWlUot1GodRCKe1aeUDfW7sdH0Bpgbcals+jNramKRn1+Pqiol+HwuoqLEkEqtM51L7++OpSP3\nOy0t7Zbvx7DGsigHWLVqFbKzs1FfXw9vb2+kpaVBo9GAYRgMGzYMALBu3TqcOXMGQqEQ8+bNQ0xM\njMWPX1JSYqvQnZYrvSGqqlQ4dKgYK1acQkFBHRgGGDcuFo8/norOnX1anWPpSv2+FbfSb4ZhjH6J\nMufSpXocPVqM77/PAcuymDgxHgMHhiEuznRpDVux5e9bpdJhw4ZsvPGG4ZMZnnmmB558sovelGVx\nsQInT5bhs88yUV3dnETPmZOCzp2l8PIyX1/OEob6feVKAwYP3mxyraOnpxsOHEhDWJhtagLm58vx\n3nun8NNPeS2Fp8PDxXjrrbswYECQ3hTwraL3d8fSUfsdEhLSpvs5VWJna5TYtV8ymQqvv/47fvwx\nV+8al8vgq69GY+DAG28CV+n3rbJ3vzUaFiwLuLk5dq2rLfudnV2D4cO3mmyzc+ckpKbe2NiVny/H\nrFk7cflyrV7buXPvwPPP94C39+0nd4b6rdUCS5f+jvXrzxm93/PP98Jzz6Xe9vMbUlgox+TJ21FU\nZHg6+KOPhmHixJg2f6kA6P3d0XTUfrc1sXOqcieEGJORUWkwqQOaj5h6/PF9KClpW2Fc0nY8HuPw\npM7WDh0qMttmx47LLWvtFAotXnnliMGkDgDWrTuL48dLrRrjzbhc4LHHuiIlxdfg9V69gjBtWqLN\nnn/fvgKjSR0AvPjiYRQVGa+FSAi5PZTYEaenVGrxySdnTLapq1Pj4sVqO0VEOgqGYXD2rOkTOwAg\nI6O8ZQTqypU6/PbbVZPtV6w4hbo6wxtQrCEszAMbNozEBx8MRUKCD8RiPlJS/LBmzXB8+ulwm9UD\nrKpS4YMPTptsU1+vRm4u1R8lxFacbvMEIf8ml2tw/nyV2Xa5uTUIChIhKkrs9Lv6SPsRFmb+xRQW\n5gmGYVBVpYJSqcW4cbHYs+eK0XVuFy7IUF2tttpaO0NCQjwwaVIMhg+PQGOjBu7uPHh62vYjX63W\noaLC+Fm/19XXW7bBgxBy62jEjjg9Pp8DicT8UUZNTToMH74V7777F8rKXH89xrVrjThzpgpnzlSh\nrMz8H1Ny61iWxT33RJlsIxbzMXt2Cj755CyGD/8e06alo6ioHosW9UFaWpLB+zAMWm32sSWxmAd/\nf6HNkzqgeWd0aKj5s3wlEip2TIitUGJHnJ6Xlxsef7yryTYcDgMejwOWBT799G98+222yfbtWXW1\nGt9+m4MRI77HmDE/YsyYHzFixA/46quLKC11/YTWHpqadMjLq8Mff5RDp2OxYsUgeHvrJyN8Phcr\nVgzCnDm7sXz57ygrk0Oh0CAjoxxvvfU76uvVmD49We9+o0bFICDA9c5d9fHhY8GCHibb+Pq6Iz6e\nTgsixFZoKpa0CwMHhiEkRISSEsOLrmfN6tTqRIQVK05i1KhIhIUZP3vY3hQKLa5cqUNNjQoeHm6I\njvaCRNJ6Kk6rBQoLG1BaKgeXyyA83BOhoaKW9VtyuQYrV57GunVnW92vqkqJF188jPPnq/DSS70g\nFttuis/VFRbK8f77f+H773Og1Tb/3ENDPbFkSX+kp+e1rJ9zc+Pg3XcHYvXqv1Bd3WjwsXbvvoyX\nX+4Ld3celMrmun8cDoMnnkg1WH/RFQwaFI4uXfyQmVmpd43DYfD++0MQFERn/hJiK1TuxMW50jbx\ny5fr8dJLR3D0aHHLbR4ePMyZ0xnl5Qr88ENOq/abN4/DnXcG2TtMg7Kza7BkyTH8/vuN12B8vAT/\n+c/d6NXLHzweB6WlSqxdm4kNG7KgVmsBAF5efLzwQm9MnBgLHx8+MjKqMHbsjyafa/v2iejRw8+m\n/XE21nqdFxUpMG3aDqM7Wt99dxDi4ryhUmkRGuqJ6moVxo//yeRjJiZK0a1bADZtugB3dx4+/ngY\nhgwJs0qxYmd9fxcXK/Dddxewdm1myyklffuG4KWXeiM11e+2++6s/bY16nfH0tZyJzRiR9qNmBgx\nvvjiHpw7J8OJE6XgcBhotSx++OEi8vL0d9ldL4x6XWmpEufPy5CdXQWRyA2pqQGIi/Oy+ejWxYu1\nmDjxZzQ0tN4FmZtbg7S0dGzePA7x8RLMn38Qx4+3/vJRV6fGa68dRUlJA55/vjv27r1i9vl27LiE\nnj39b6tOWEf1229XjSZ1APD660dx8GAawsObR4J37za/E/viRRlefbUvhgyJwB13+CE8XIT2dsR1\nWZkSly7VQqHQwMdHgLg4id5o881CQz2wcGF3TJ2ahJqaRvD5XISFieDhQX9yCLE1epeRdsXDg4vY\nWC88/fQBFBUZ/wbH5TIIDr4xDXv6dCVmz96tN2U2bFgk/vOfOxESYpsK/E1NOnz66d96Sd11Oh2L\nV145jJdf7qeX1N3sk0/OYNKkeItKuly8WH1bp0p0VNXVanzwQYbJNgqFBhcv1rQkdm5u5qdT+Xwu\nOnWStsvpx8ZGLX79tRgvvngYlZU3NujExUmwcuUQdO9uuFbedWFhHjY73YIQYphrLvIgLs3XV4BF\ni3qZbHP//YmIimouU5GTU4cpU9INroPav78Ar712DHK5Ru+aNRQXK/D99zkm2wQFeWLDBuOnBFx3\n7Fgx0tIS4elpeoQxIcGHkro2UKt1KC83X+T65lIdsbES8Pmmj8eaMSMZ/v7tL6kDgMOHS/Dww3tb\nJXUAkJdXg/vv34asLKpHR4izocSOtEuDB4cb3G0IAF26+GPx4j5wc2PAMAz27Lmid2D9zfbsycel\nS3U2iVOp1LQswDfGx0eIa9fMV+I/f16GL7/MwhNPdMOcOSlG240bF0uJXRu4u3MRFnZrpToiIz1N\n7gIVCLiYMSMZ3Ns7GtUhKipUePnlI0avq1RafPxxBpqa6LVGiDOhxI60S1IpH6++2gdbt47HmDGx\niI2VoF+/YKxffw82bBiJ2FgpgObpta+/Nl/65Px5mU3iFIvdzB54XlYmR1ycj9nHCg72REZGOd55\n5ySKixswY0YnvTYzZ3ZCQoJ3m+PtyLy83PDMM6ZLdXh58ZGYeKNUB4cDzJ6djAULeujVpQsI8MCm\nTeOQnNw+S3tcuVKH0lLTXzi2b7+E4mI6HowQZ0Jr7Ei75e3thv79A9G7dwCUSi0EAi74/OY/rtfP\n7dTpWKPV/2+mUmltEmNoqAcefPAOk0eiabU6zJjRCenpl4y24XAYeHvzW6YB9+8vwKuv9oNAwIVK\npYVUKsTChb1w331J8DQ/6NRuMUzzhhkejwOdzvzv9VbdeWcIevQIxF9/XTPw3MD77w/RW4/ZXLst\nFRMnxiEnpwZKpQaBgR5ISJC061p1jY3mlyfodKzN3juEkLahxI60ezweA7HY8EtZLHbD3XeHmV3n\nFhtrm1EuhmEwc2Yytm/PQ3Gx/sHo7u48vPnmXYiI8MTUqUnYtOmCwcd58slueuVcduzIw86dk6BU\nahASIkJQkLvLlgVobNQiN7cWu3ZdwYkTpfDzc8f06clISfGFv7/1TjEICnLHmjXDsXnzRaxZc6Zl\n00uPHoF45ZW+6N7dcBkZNzcO4uK8EBfnZbVYHE0qNZ+UisV8eHnx7RANIcRSlNgRl+bmxmDWrE4m\nE7vQUE8kJJifCm2rqChPbNkyDl9+mY0NG85BpdKCw2EwfnwsnnqqW8tU3csv90bPnkFYseIUysqa\np7cSE6WYPj0ZBw8W4ty51gVfL1yQQSoVIjCw/Y4KWUIu1+Dbby9i6dLjrW7fufMyUlMD8Omnw5GU\nZL1hypAQdzz7bComT05AdfWNUh0iUcf6uIyJ8UK/fiGtai/+2xNPdENIiAet6STEiVCBYhfnqiM4\n5tzc78ZGLb77LgevvnpUr51EIsDWrePRqZPt10GxLIurVxVoaFDD3d0NYWEiuLnpFzSrqFAhO1uG\ns2crUFBQh59+ym0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atNuP+CZSqRSVlTdKAMhkMvj4+LRqc/MH2tChQ/HNN98YfTxDP4CO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\nIiIiJ8HEjoiIiMhJMLEjIiIichL/B2FQT5nvKr0nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a8c6c50898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(X[:, 0], X[:, 1], s=100, c=y)\n",
    "plt.xlabel('feature 1')\n",
    "plt.ylabel('feature 2');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because of all the noise we added, the two half moons might not be apparent at first glance.\n",
    "That's a perfect scenario for our current intentions, which is to show that decision trees are\n",
    "tempted to overlook the general arrangement of data points (that is, the fact that they are\n",
    "organized in half circles) and instead focus on the noise in the data.\n",
    "\n",
    "To illustrate this point, we first need to split the data into training and test sets. We choose a\n",
    "comfortable 75-25 split (by not specifying `train_size`), as we have done a number of times\n",
    "before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, random_state=100\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's have some fun. What we want to do is to study how the decision boundary of a\n",
    "decision tree changes as we make it deeper and deeper.\n",
    "\n",
    "For this, we will bring back the plot_decision_boundary function from [Chapter 6](06.00-Detecting-Pedestrians-with-Support-Vector-Machines.ipynb),\n",
    "*Detecting Pedestrians with Support Vector Machines* among others:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def plot_decision_boundary(classifier, X_test, y_test):\n",
    "    # create a mesh to plot in\n",
    "    h = 0.02  # step size in mesh\n",
    "    x_min, x_max = X_test[:, 0].min() - 1, X_test[:, 0].max() + 1\n",
    "    y_min, y_max = X_test[:, 1].min() - 1, X_test[:, 1].max() + 1\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
    "                         np.arange(y_min, y_max, h))\n",
    "    \n",
    "    X_hypo = np.c_[xx.ravel().astype(np.float32),\n",
    "                   yy.ravel().astype(np.float32)]\n",
    "    ret = classifier.predict(X_hypo)\n",
    "    if isinstance(ret, tuple):\n",
    "        zz = ret[1]\n",
    "    else:\n",
    "        zz = ret\n",
    "    zz = zz.reshape(xx.shape)\n",
    "    \n",
    "    plt.contourf(xx, yy, zz, cmap=plt.cm.coolwarm, alpha=0.8)\n",
    "    plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, s=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can code up a for loop, where at each iteration, we fit a tree of a different depth:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
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G+3pcz9dXB0iMCtHR6upVoqL8Pa4XFmbC7pRbIyE6Wr0NoqMDPK4XFmbC7pIY7QrkX7GT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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a8c70caa90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "plt.figure(figsize=(16, 8))\n",
    "for depth in range(1, 9):\n",
    "    plt.subplot(2, 4, depth)\n",
    "    tree = DecisionTreeClassifier(max_depth=depth)\n",
    "    tree.fit(X, y)\n",
    "    plot_decision_boundary(tree, X_test, y_test)\n",
    "    plt.axis('off')\n",
    "    plt.title('depth = %d' % depth)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we continue to build deeper and deeper trees, we notice something strange: the deeper\n",
    "the tree, the more likely it is to get strangely shaped decision regions, such as the tall and\n",
    "skinny patches in the rightmost panel of the lower row. It's clear that these patches are more\n",
    "a result of the noise in the data rather than some characteristic of the underlying data\n",
    "distribution. This is an indication that most of the trees are overfitting the data. After all, we\n",
    "know for a fact that the data is organized into two half circles! As such, the trees with\n",
    "`depth=3` or `depth=5` are probably closest to the real data distribution.\n",
    "\n",
    "There are at least two different ways to make a decision tree less powerful:\n",
    "- Train the tree only on a subset of the data\n",
    "- Train the tree only on a subset of the features\n",
    "\n",
    "Random forests do just that. In addition, they repeat the experiment many times by\n",
    "building an ensemble of trees, each of which is trained on a randomly chosen subset of data\n",
    "samples and/or features."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementing our first random forest\n",
    "\n",
    "In OpenCV, random forests can be built using the `RTrees_create` function from the `ml`\n",
    "module:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import cv2\n",
    "rtree = cv2.ml.RTrees_create()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The tree object provides a number of options, the most important of which are the\n",
    "following:\n",
    "- `setMaxDepth`: This sets the maximum possible depth of each tree in the ensemble. The actual obtained depth may be smaller if other termination criteria are met first.\n",
    "- `setMinSampleCount`: This sets the minimum number of samples that a node can contain for it to get split.\n",
    "- `setMaxCategories`: This sets the maximum number of categories allowed. Setting the number of categories to a smaller value than the actual number of classes in the data performs subset estimation.\n",
    "- `setTermCriteria`: This sets the termination criteria of the algorithm. This is also where you set the number of trees in the forest."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can specify the number of trees in the forest by passing an integer `n_trees` to the\n",
    "`setTermCriteria` method. Here, we also want to tell the algorithm to quit once the score\n",
    "does not increase by at least `eps` from one iteration to the next:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "n_trees = 10\n",
    "eps = 0.01\n",
    "criteria = (cv2.TERM_CRITERIA_MAX_ITER + cv2.TERM_CRITERIA_EPS,\n",
    "            n_trees, eps)\n",
    "rtree.setTermCriteria(criteria)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we are ready to train the classifier on the data from the preceding code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "rtree.train(X_train.astype(np.float32), cv2.ml.ROW_SAMPLE, y_train);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The test labels can be predicted with the `predict` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "_, y_hat = rtree.predict(X_test.astype(np.float32))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using scikit-learn's `accuracy_score`, we can evaluate the model on the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.83999999999999997"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "accuracy_score(y_test, y_hat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After training, we can pass the predicted labels to the `plot_decision_boundary` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5MrOclIgejDAFAEHyfjnL9IwzBuudd0LbXPKv/7NFFfnHR6Cq6DDcHlXv2BFy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\nHCBMAQAAOPD/Adzm3eAn/CORAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a8c8530e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plot_decision_boundary(rtree, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Implementing a random forest with scikit-learn\n",
    "\n",
    "Alternatively, we can implement random forests using scikit-learn:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "forest = RandomForestClassifier(n_estimators=10, random_state=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we have a number of options to customize the ensemble:\n",
    "- `n_estimators`: This specifies the number of trees in the forest.\n",
    "- `criterion`: This specifies the node splitting criterion. Setting `criterion='gini'` implements the Gini impurity, whereas setting `criterion='entropy'` implements information gain.\n",
    "- `max_features`: This specifies the number (or fraction) of features to consider at each node split.\n",
    "- `max_depth`: This specifies the maximum depth of each tree.\n",
    "- `min_samples`: This specifies the minimum number of samples required to split a node."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then fit the random forest to the data and score it like any other estimator:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.83999999999999997"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest.fit(X_train, y_train)\n",
    "forest.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This gives roughly the same result as in OpenCV. We can use our helper function to plot the\n",
    "decision boundary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SY7OtxsQM/eyXZ+jeu9+X3U7p3/9+gcrK9mvt2oqDn7MsW/ff874efPQ8rd0enY2dAThH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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a8c8c898d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plot_decision_boundary(forest, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Implementing extremely randomized trees\n",
    "\n",
    "Random forests are already pretty arbitrary. But what if we wanted to take the randomness\n",
    "to its extreme?\n",
    "\n",
    "In **extremely randomized trees** (see `ExtraTreesClassifier` and `ExtraTreesRegressor`\n",
    "classes), the randomness is taken even further than in random forests. Remember how\n",
    "decision trees usually choose a threshold for every feature so that the purity of the node\n",
    "split is maximized. Extremely randomized trees, on the other hand, choose these thresholds\n",
    "at random. The best one of these randomly-generated thresholds is then used as the\n",
    "splitting rule."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can build an extremely randomized tree as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "extra_tree = ExtraTreesClassifier(n_estimators=10, random_state=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To illustrate the difference between a single decision tree, a random forest, and extremely\n",
    "randomized trees, let's consider a simple dataset, such as the Iris dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()\n",
    "X = iris.data[:, [0, 2]]\n",
    "y = iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, random_state=100\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then fit and score the tree object the same way we did before:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.92105263157894735"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "extra_tree.fit(X_train, y_train)\n",
    "extra_tree.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For comparison, using a random forest would have resulted in the same performance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.92105263157894735"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest = RandomForestClassifier(n_estimators=10, random_state=100)\n",
    "forest.fit(X_train, y_train)\n",
    "forest.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In fact, the same is true for a single tree:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.92105263157894735"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree = DecisionTreeClassifier()\n",
    "tree.fit(X_train, y_train)\n",
    "tree.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So what's the difference between them?\n",
    "\n",
    "To answer this question, we have to look at the\n",
    "decision boundaries. Fortunately, we have already imported our\n",
    "`plot_decision_boundary` helper function in the preceding section, so all we need to do is\n",
    "pass the different classifier objects to it.\n",
    "\n",
    "We will build a list of classifiers, where each entry in the list is a tuple that contains an\n",
    "index, a name for the classifier, and the classifier object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "classifiers = [\n",
    "    (1, 'decision tree', tree),\n",
    "    (2, 'random forest', forest),\n",
    "    (3, 'extremely randomized trees', extra_tree)\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then it's easy to pass the list of classifiers to our helper function such that the decision\n",
    "landscape of every classifier is drawn in its own subplot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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VMIkmskNSQz103t6oLSzEgNmzkfzhh03G+JWeOIGT69fDPSwMw956C3sXLICT\njw9QX9cxQRMRdTDJYIDazQ1Ovr6IfuQRHPzHP1B//rxFmeLfhsr4DxuGwW+8gb0LFkDn6QnTmewO\niJiIqOMpVSoAQPCdd6JTZCQOLl7cZIKxouRkSLKMyIkT4dm9O4599hmUalVHhGsXmEQT2SFDbi6i\n7rkHIXfdhYT582G4wu3Z5zMzsefttzH49dehVathONr6bN5ERDelUycRN3UqZEdH7HrjDeAKV0nO\n7tqFmsJC9J89G24+3kB21nUMlIjIfqiqKtH9sccgq1RIfO+9FssJkwmpX36J4DvvRPcpU6BTqXCr\nXovmxGJE9shohFvXQCR/9NEVE+iL9JWVOPb553D1873iSSMR0c3MVFYG3359sW/hQqv6wvMZGag+\nexYaPW/jJqJblzEjHYEjR+Lof/5jVfnTv/4Kj8hISFkn2zky+8UkmshO1RQVNZn85kpKT5xAbVlZ\nO0ZERGTfJK0WpSdSYbRhbHPamjXQq9TtGBURkX1T+Pnj5Pr1NtU58sknkAIC2iki+8ckmsgOKbt0\nQebmeJvrnd6dAKWXVztERERk/+TwCCRbeSXlImN9PSrKzgOS1E5RERHZt8YuXZBpYxJ9Pj0dtUqH\ndorI/jGJJrJHTs4oy8y0uVpJRgYkF9d2CIiIyP4JRx0qz5yxuV51YSEkjbYdIiIisn/6+gbzygY2\n1autbYdobgxMoonsVRuuikiSxDHRRHTLEm2d4ka6UJuI6NbUxv7vFu42mUQT2aPKCnhGR9tczSsm\nGqaK8nYIiIjI/sk1NXALCbG5nouPL8Rly7kQEd0qVGo1ZAfbb81WO+naIZobA5NoIjvUmJ+P0GFD\nba7XtV8/GEtK2iEiIiL7Z0pPQ+zjj9tUx0Gng4urM+/iIaJblvJsLiLHj7epjmePHtDW17VTRPaP\nSTSRnXI06OFhw9Vonz59oKmuaseIiIjsm2hoQGcfbzjorL860vP//g+Kk7fuMi1ERMa8PISNGmlT\nndjHH4MxLbWdIrJ/TKKJ7JQx5TiGv/wSNO7urZbVenpi6DPP3NKdGRERAMjHj2LMonchK5WtlvWJ\njUVo714wlvIOHiK6talzszH01VetKtv94Yfh1mgA2jAZ2c2CSTSRvTIaIe/fi7uXLIFXbGyLxXz7\n9cPdixZB2pcAmEzXMUAiIvtjqq6GLisTd3/wPpz9/ZstI8kyIv84HrdNmwrTgX3XOUIiIvtjys+H\nn8oBty+sscjRAAAgAElEQVRcCLVr8yu9KDQaDJz1HKJje8OUkX6dI7Qvrf9MS0QdRjQ0QNq9AyMf\neQh1Tz2F3IMHUZx+odPyjo5GQFwcNFUVMO7eAcEEmogIAGA6fx6aw0m4a/bLqAGQtX0HKs+dg1Kj\nQZe+feETHgZVfh6M+/d2dKhERHbDdCYHnV1dce87f0VFdQ1Obt2KurIyqJ2cEDx8ODr7eEORlQlj\nakpHh9rhmEQT2TuTCcYTKVABiPDwQFTInRc2V5TDeHAfbt0baYiIWiYaGiCSE6EF0DsqHFJcb8Bo\nhLG0BKZ9e9h3EhE1w1RRARzcDxeFAgMG9AVUasBgQGP+WYjsLPadv2ESTXQDMZaUcPZtIiIbNebl\ndXQIREQ3FqMRhtzcjo7CbnFMNBEREREREZGVmEQTERERERERWYlJNBEREREREZGVOCaa6Aai6NwZ\nsqsbIMSFicXKyjo6JCIiu6f08YWk00EyGtFYUgxTdXVHh0REZN8UCjj4+UFSayD0ejQW5EPo9R0d\nld1gEk1k72QZiogo1Dk748yhRBTvOwhJkuAZFYXAAYOgriiHMT0NEKKjIyUishuSWg0pujtqAJze\nnYCKM2egUKsR0K8fvHv0gurcWTTm5nR0mEREdkV2cYWIjEJldQ2ytm1DbVkZVM7OCBk2DJ28vaDI\nOgljcVFHh9nhmEQT2TFJrYYYOBjbly5DYWKixb4zO3ciCYDfwAEYPG06pH0J/IWQiAiA7OaO+sgo\nbHvrbVSdOWOxL3fbNkiyjMg//hE97rgdJq4VTUQEAJADA1GqccTuV15FQ3m5xb7sTZug1GrRd/p0\nBEZFw5R6ooOitA8cE01krxQKmAYMxoZZzzdJoC+Vt28//vvyyxADhwAyP9JEdGuTnZxQGxaODTNm\nNkmgLxImE1LXrMGu5R9D6jfgOkdIRGR/ZB9f5BtM2NJMAn1RY10d9i1ejNTDRyGHR1znCO0Lr0QT\nXSWFrx8auwajuqIcRqMRClmGk5sblLk5MOada3u70THY8u67qLdi3HNtURES3n8ftz34AIy3+C+D\nRHQDkCQogkOg9/JGzflyCGGCUqWCTqeDIjMDxrLSNjdt6t4Tv856HqbGxlbL5icm4lT//gjt3Bmm\n0rYfk4joulAqoYiMQp1Gi/rKKggAKq0GOqUCIvUERG1tm5tuCAzCrmnTrCp7bPVq+P3973BWKACj\nsc3HvJExiSZqI1mngzGuL45v2oy0Rf+A8ZJbqWUHB0SOvw+RY8dCmZzYpklsalUalJywPiHOP3QI\n9X96Ag42H4mI6PqRPb3Q0C0ch77+Gtlbt1rM56Bydkavxx5D18FDgQP7ACsS4UtJajVKC4tgqKmx\nus7RlSsRvPgfQClv6yYi+yWHdkOloxMSP/0UxceOWexz8vND3JQp8I6Mhkg6ZHPbCj8/pG7fblOd\npBUrMOrx/4PxRIrNx7sZ8N5PojaQHB3R0CsW65/5M1K++soigQYAk8GAE998i/Uzn0Z9916QnZxs\nal/p64tTu3fbHFfuoUQoPDxsrkdEdD3IXl4ode+E9VOnInvLliYTIuqrqnDwgw+w8S9zgSG32TxE\nRQ6PQNKKFTbVMdTUoLKyEpAkm+oREV0vcngEMk/nYOOsWU0SaACozsvDzrffxp5VqyENGGRz+43+\ngUj9/nub6hQfO4Y6rc7mY90smEQTtYGpdxx+mfU8DK1cYW6srcUvs2ahsWdv2w7g4oqiFNt/2Ss8\nfvzCElhERPZGllEfEoYtr86BMJmuWLT63DlsnjsXUlxfmw5hcnJCeVaWzaFV5udD0mhsrkdE1N4U\nbm4oqK1D8n/+02rZc/v2IenH9ZDDwm06hl6vh8lgsDm2hlt4uUAm0UQ2UnTqhNzDR6CvqrKqvKGm\nBtn7D0Dh4WnbgdqwZJUQgldTiMguKbqF4dCnn1jdt1WcPo3y6mpAaf3IMwlt7P/EhdpERPbGFB6J\n/e8ttbp81n//C32nzrYdpK2rpN7C3SaTaCIbmcLCccTG2wWPffkljKHdrK9QXYVO3Wwo/xuP8HCI\nygqb6xERtbcG907I27ffpjqHv1gFRWSU1eWlulo4+/vbGhqcvL0h6utsrkdE1K4UClRUVaOhwrZz\nu+z9B6Dw8rK6vEqjhqRQ2BodVI6ONte5WTCJJrJRXV291VehLzLU1KC2xvoZExvPnUO320fZGhqC\nBw1EY1GRzfWIiNpbVWnrKw1crvjYMTQ6Wj/mzpSejthJk2w6hkKjgVsn9zbd/UNE1J4UnTohLznZ\n5npZmzcD3j5Wl1fmnUPYuHE2HcM9IgKONk7+eDNhEk1kI1MrY/la0toYwMs5mUxwDQ62urxHdDS0\nDfW2hkVEdF3Y2gdeZEufK+pq4RkYAIVKZXWdmAcnQHHa9nHURETtTXJQtWnccWNtLYQNQ2GMZ3IR\nMfZOm44RN+lxmNJu3WVVmUQT2UihbNsiUgoH21aUM6Ucw6g334CDrvWrMCpnZ9z28ku37DIDRGT/\nFA5t6DslCQobbzFUpKbgjnf+atX8EJ3CwxE5bBiMhYW2x0ZE1M5EfT10bVh1Re3uDsnGicK0BQXo\n9/TTVpUNGzcOnVUOEJetTnMrYRJNZCOtDDh16WJTHUdvbzja8IsgAAiDAQ5JhzDu/WVw6dq1xXJu\noaEYt2wZFAf337IL3hOR/XPp5G7zmLvg22+Hosi2BNdUXg7XokKM/ecSqN1aXq0gcPhw3D77ZZj2\n7bGpfSKi68VYVgr/uDib68WMHw+Rk21THdOZHAT7+mDIK69AoVY3W0aSZfR8/HHEjrkDppTjNsd1\nM7HtrJ7oZiRJUASHQO/phbrKSpiEgINaDZ1SCaSdgKmmxqK4SD2BuCemYOf8t6w+RNyUyUAbbnkx\n1dRAuW8Pxr74PGogIWPTZpSePAkJgEdkJMLuuB2OxkaY9uyCuIXHpRBRB1AooIiIRJ1Wh4aaC7cb\nqhwd4WjQw5SWCnHZVRDl2TMIu+ceZKxbZ/Uhou+9B8Yjto8HNBUVwqWuFvf+dSEqa2px4qefUJ2f\nD4VKBf8BA9C1Xz9oKsphTNhlc9tERFdD0mggRcWg1iRg+G0YntbFBeqyMhizMpvMz6ATJrgEBqIy\nN9e69mUZ3mHdYNqbYHNspqxMdPHwxB+WLUVZfgFSN2xAQ3k5HHQ6dBs9Gj5h3aA8ewamw7b3yzcb\nJtF0S5O9fVEfFIxja9bg9JYtFh2XzscHsVMmwze6O8ShA+Z9oq4OXr6+cO/WDedPnmz1GK5BQfDp\n2hWmA/vaFKMwGCCSk6AF0CeuF8RtQwEhIFVVoTHxINo2ypCIqO3k8AhUOKiRvHIlio4csdjXKTIS\ncZMnoZNChumSISbGM7no/vt7kb1li1WTMwbdfjsc9Q0Xlu5rA1NVFXBwP5wVCgwdfTtMGg0kowmi\ntATGA3vB+3aI6HqT4vqiqLgESX97F1Vnz1rsCxg2DL0eegiOhXkwnTlj3m5KTcGw2S/j56efsWoC\nxH5PPw1l9qk2nx+aSoohlRTDQ63GyPF/gMnBAbLRCGNeHkz79vC88ze8nZtuWbKvH4q0jlg/bRpO\nx8c36ZhqCgqwe+Ffsf2DDyANGWZZOfEg7nj9NXQKv/Ji9m4hIRg9dy6QePCaxNx45gyMqSdgTEtF\n47mzrVcgIrrG5KgYpB0/gV9feKFJAg0AZWlpiJ/9Cg7v3A25Z2+LfYrEg/jde/+84m3WABA0ciT6\nPXA/xLWY58FohOFUFownUtCYngpjSfHVt0lEZCNp4GAkfLEKO+bNa5JAA8CZXbuwYcYMnK1tgNw1\nyLxd1NdDdyYXYxYtgtzK0MC4J59EUBdfmPLzrzpe0dAAQ2YGjCdSYEhPg6mq8qrbvJnwSjTZPYW7\nO0RoGISDEoAE+XwZjJkZVzX+V3JwQE0Xf2yfMbPVssXHU7D7w39h2KOPwHT08IWNJhPE7p24/bln\ncb6iAkkrVqIsLc1cxy0sDH0mT0Ind3eI3TuANs5KS0TUVgo/P4iArhCSBEkISAX5MNo4Rq5Jm56e\nyMkvwNFVq1otm7F+PRw9PRDRxR/G3370E7W1cDh0AOMWvYvC06eR9MmnqL1kUq8ugwej5wMPwMlk\nhGjj3TtERG0mSVCEhMLkeWGNZclkgpRz+qonH5Qjo3Dgm2+Rt39/q2X3LFqE0YsWwc3REaL2wvKo\npqJCuDU24vcffoDc5GQc/WIVDL/N2i0pFAi75x5EjBkNTUkRTOlpV2qerhEm0WS3ZF8/GAKDkHP8\nGI7OeQ0N5eUXxnnExqLXxIlwddbBlJQItGEssBwVjb1Ll1ldPv/QIdRMehxaSfrfFWuTCeLQAbg7\nOGD01CehV2tgbGyErFBAbTDAlHYC4mSGzbEREV0NOSwc9a7uyNqxA2l/XwJjfT1kpRLBd9yOyLvv\nhs7YCNPRpleQrWEMDcPBPz9rdfkjn69AyL8+hHzJnTOivh7S3gT46XTwe/N16AVgEgJKBwc4lJ+H\nMeVom5fDIiJqE0mC3CsW1QJIWbcOuTt3QhiNcNDpEPXHPyJo0EBoSophOn2qTc3XO7sge8sWq8vv\nXbIEv5vzKkTSIfM2U1kpFPv2ILRTZwT/fREMjY0QQsBBpYLy3DkYDx0AV7u/fphEk12SQrsht6QM\n+6ZOtdguTCYUJCaiIDERTn5+GLNwART790I0NNjUfq1KbXHl2BonflyP/rcNhfGyDlQYDDAeOwoF\ngIvzznKsHRF1BCmuLw5v2oyM9esttpsaG5G18VdkbfwVfv37Y8hTT0Hs3W3V+Dpz22o1SguLYKy3\nfj16YTSiMOsUujg5wXTZWqemmhog8ZDFiQj7TiK67mQZ0pBh2LZ4MYqPWc44baipwdGVK3F05UrE\nPPQQovv3gzhm24+QCl8/ZO3YaVOd6rw81JiE5cWb35jKSoEDpbh00UD2ndcfx0ST3ZF9/XCmpAz7\nliy5YjmjwYB9/14Oxe1jILUwFX+zFApU5BfYHFf21q0Q3j421yMiuh6k6Bgkbfi5SQJ9uaq8PBz+\neQOUI0YBNiw5pfTxxakdO2yO6+TmzZB9/WyuR0R0PUj9B2Lz2wuaJNAWZRQKnN23Dzm5Z+AQ18eq\ndegvEj6+OLV5s81xFaalQXZysrkeXR+8Ek12xxDYFfumTmt2n6RQIOSuu+DTr9+FX+ny85H8zTfw\n7dkDnX18oMzJhjHvXIttK/y6wBQWjvordJQtEUYjZyQkIvsky6iSFcjauLHZ3Q46HcLvvx9uISEo\nz8pCfVkZjm78FV1694arizMUmRkwlpU23/bFMYJRMWj4b/PtX4mhpgailclwiIjaylhwYSZrhU+A\nzXUVbm44fTwF5VlZze539PJC5IMPQuPujrL0dJRkZKC+qgr+PWPhpJQhUk+Yxy03oVRCERkFYxd/\n6C9bLtUaDVVVkHy8ALS+mgFdf/xWI7ui6NQJOUePNrtP7eqKgXPmIP3775Hwl79Y7EtfswaygwNi\nJk5E1LChMO3fa3n7iyRBHjAIJ3YnIPUfSxD39NO2x6ZSQQaYSBOR3VF0C8PRb75pdp9baCh6PfUU\njnz8MVJWrLDYd2L1aqicndHv6ZnoEh1jsSQVAEgqFTBwCPatXIn6H9ZB27mzzbGpXF0hNzbydkMi\nahdXMw7YFB6JI7NfaXZfwIgR8B8yBIeXL0ddseWs/kcBOPn5YehLL8GlrASmy1ZMkV1doe/eC9sX\nL4bvgAFQu7qi1oahMACgdXeHqK+zqQ5dP7ydm+yKCAnF0VWrm2xXaDQY9Npr2LdwIQoOHGi2rslg\nwLEvvsDOj/4Nqd8Ai31y/4HY9e/lOLZyJRrr6uDQhttjIu77A6TcbJvrERG1N72rG/L2N+0bnbp0\nQY/Jk7FzzhyUt7Cuvb6qCgl/fQcnkg5Djoj83w5Zhhg0FD/Pno2crVtRcuwYfPr2tTm2qHHj0Jib\nY3M9IiJr+d/W3XxF2hbVtXVoqKhost1v4EB4REdj74IFTRJoc928PGycNQtFShXkS4b7yY461EfF\n4KcZM1CSkoLcbdsQMnaszbF5R4RfmDuC7BKTaLIrQunQbGfWc8oUJC5d2uy+yxUkJSF97z4ofH0B\nAAofX6Tv24f8xMT/lTl4ED79+tkUW7fhw2HMy7OpDhHR9WDQ65vd3vNPf8Ket96CsGJJwONffoky\nIV24+gxAEdMD299917wElVGvR2N9PVQuLlbHpdBo0NnH2+bJH4mIbFE0cX6b6hma65skCaHjxiH5\nww+tamP73LloCAoxPzb17IWNL7wI429tV5w+DdfgYJvico+IgKPBYFMdur6YRJN9kdBksgZJlqH1\n8Gh2YfqWpHz1FRq7XuiwjMEhOL76S4v9p375BZETJkB2cGiuehPh994LTWXrCTwRUUeQmpnkRufj\ng9qiIvOJnDUOLl8OOSoawIVVDEpSLG/vTvvmG8TOnGl1e4NmzYKCS/0RkZ1qru/sOmoUTm/aZH0j\nQiBjSzwUfl0urGKQXwB9leU45pwtWxA5YYJ1Mckyhjz3HExpJ6yPga47JtFkV+TycnjHxlpsCxg+\nHLnbttnUjlGvR1lBIWSdDqX5BTBedpVGGI1IWrYMQ+fPh0KjuWJboWPHovedY2DKSLcpBiKi60Wt\nUjUZphJ+//1I//Zbm9qpOHUKNbISSl8/nNy+vcn+6nPncG73bvT5859bbav/M8/Az0kHY0mJTTEQ\nEV0vjq5N76zpMngwzu60bUmq9LXr0BgQCDkiCkmffdZk/9ldu6DQaNDt3nuv2I6sVGL0okVwzM6C\naOEOI7IPnFiM7IoxIx29HnoIm5KSzNu8eveGUa+H74ABMOn1KE1NRc6WLRCmK0/xVZKZCd+oCJSk\nNp/8VubmImnZMgyaMwcV2dlIX7PG4pfDLoMGoeeECXAyGmBKPHhtniARUTuQT2ag5//9HxL/9S/z\nNpfAQEQ88ABUzs4w1NYif/9+FBw61Gpb9dVV0Lm7o7CFSR7P7toFQ00Nhr39NgoSE5G1YQNMv912\nKCkUCBs3DhFj74S2qACmrMxr8wSJiNqBpqoSPn36oOCSIX+azp0R+/TTcNBq0VBRgez4eFScOnXF\ndkwGAxrq6iA5OqIiO7vZMikrV6LbvfdiyNy5yNmyBWd37TLvUzo6oscjDyOo/wA4pKXAdP78NXl+\n1H6YRJN9MRrh6uwEna8vtJ06Ifz++9FYW4u0b75BTWEhFGo1vHv3xqDXX0ddSQmOfPyx+eTtciaD\nAZJSAVNjY4uHq87Lw+4334RL167o+eSTUDg4XFjORa1GYHQUGjf/2mqyTkTU0UxlZQgcNASH1Wr4\nDx2KgOHDUZaejswffkBDRQUcdDoEjBiBIfPmoeTYMaR/912LbQkBQJKu2HcWJiWhMCkJXrGx6Pfi\ni4DJBEgSHL280MVRg/oD+7mSARG1i8YWJhATuLDclS1LXRkz0tFnymT8nJiI6EcfhXtYGHLi45G7\nfTsaa2uhcXdH6Lhx6P7448jetAnnEhJabEsIWK4M04yT69cja8MGBI4ahYFz5pjPMT2iouCckYrG\nvbvZd94gmEST3RFJibhr2TKc3rULe996yyKJbaytxbk9e3Buzx64BgVh2FtvIWH+fDQ2s0afW2Ag\nDEXFcAsMbPWYlTk5OLR4sfmxR/fuCHB+7MKJIRHRDUCZcgz3rFiBlG++we4337TYp6+qQtZPPyHr\np5/QZfBgDJg9G/v/9rdm21FpNZCqq+ASGNji2qkXFSUnoyg52fy495Qp8PJwv/onQ0R0BaoPf2h2\nm37GeNsaMpmgKy7EPatW4cDixTixapXF7rrSUhz/bWnAqIcfhnNAANK+/rr5mLQayCYT1G5uaCgv\nb/GQwmRCTnw8cuLjzdvGLFqExvx822KnDsUx0WR35M6dkZ+YiMT33rviVeCK7GwcXLwYg157rdn9\nXt26wVRWCq9uoTbH0OuhiTCd5G2IRHQD8euC1G++RubatVcsdm7PHpz+9VfEzpjRZJ/G3R1OGg0a\nc7IR/fsrj91rTmDfvhwDTUQ3FOHbBXsXLkThJUMJm5P65ZcQjY0IGjOmyT7/IUOgOl8GKTMdvSdP\nsun4Co0GLs2MzSb7xiSa7I4hOAQJ77xjVdnaoiLk7d3bZLmqwOHDoTUZIQYNgR4SAkaMsPr4SkdH\nuHt4cEIHIrpxSBKqVRqkr11nVfGiw4eh0GigdnW12N570iQo6utg7D8IClc3aD08rA7BIzoa2vqm\ndwUREdkr2cUVZzMyUHLCupmw07/7Dl2GDGmyveeECYBGg7puEejUvUeTlWauJGbiRChOXfmuH7I/\nTKLJrijc3JCXcqLVMSWXOvXLLwi56y7zY62HBwa88AKO7tqNtTNm4uennkLI2LHQdu7cemOShDsW\nLoCUmtJ6WSIiO6HoFoZjVxjn3Jz0b79FxCVLrvgOHAD/YcOwd/0GrH3qKcTPmoV+L7wAWdn6yC8H\nJyfc9tJLMKal2hw7EVFHERGRSP7sc5vq5O3dC7+BA82P46ZPh4O7O7b851OsnzEDB/7+d/SdNcuq\ntlyDgxExdCiMRYU2xUAdj0k02RURGoYjX3xhWx2jEabGRkiyDNeQEIz77FNsfeUVHF+9+sKkY0Jg\nz9tvY8Ds2XANCWmxHQedDmOXLIFLfj5EZeXVPhUioutG7+aOs7t321Sn6uxZ6Ly8AABBd9yBwS/P\nxoYnnkDOb0sKNpSX4/BHH2HoW281uWJ9KZ2vL8YtWwZl0kHgCpORERHZm5r6etSXldlUJ3vTJgSM\nHAlIEvo9+yx84+Lw05QpKEm5cAGmNDUVBQcOoP/LL0OhUrXYjk9cHMb85U2IfS1PVkb2ixOLUZsp\nPL1gDAlFXW0tTOERyK10gKKyCMLFEwLW38ZyKeHgYHNnBgCGmhqM+/BD6JydkPjxxyi97LYcQ3U1\ndr3+OmIefxzOjz+Os7t2oSQlBSaDAc7+/ugxYQLcO7tDOnECpiom0ETUfhRdg2Dw8UV9TQ1EeCTK\ncvKgCPaASesCtLHvNLRx+InKxRn3fvghtJ3c8ctzz6H+smVVKnNycGDRIvR44gk4aLU4vWkTKnNy\nIMkyOkdGIuYPf4CzygFi/x6YOASGiNqLLEMRHoEGZxc0VETCcHAb1J09oA4Mg1GpRlv7Tn19g811\nhMkE18BA3PfRR5AdHbHuySebrGZwdvduVOfno9+LL6Kxvh6nfvkFdUVFUGg06DJgAMJvvx2O+gYY\nd+2w6e5Lsh9MoslmkosLGrv3Qubu3Tj+3jIY6+vN+1yDgxH3/IvwHDgM8AyArZ2aJEkXxpHY2KFo\nnZygOZyEhrg+yPxpQ7NljHo9jn78MSBJ6DJ4MILGjIFCqUT42LGQ4n+FOGkAuzEiai+ynx/q/ANx\nYv1POLVxo8XEiT79+6PXzD/DLXYAhIunzW1LNoy/u5RWrYEq8QCqevZGZW5us2Xqy8pwaPFiKFQq\nBI4aBc8ePSApFIgeMxqNG/8LE08AiagdyRGRqFCqcOTLLy3Wc5ZkGSHj7kHMlD9BG90HQuVoc9tt\n7Tt1ChkOJ47jjEJlcR58qfKsLOxbuBAqFxd0veMOaIcMgcrFBWE9usOwaweMbToy2Qsm0WQT2c0d\n1SGh+HXmTBgbmv56V3H6NLY9MxPecX0w/IN/A762zYwtVZTDs3t3FB87ZlM9Z09PSNUVKEhPbz0B\nFwLnEhLMa/2Z6usRE+gPY2GBTcckIrKWHNAV5xr0SJg2vdn9BQcOoODAo4iY+DB6zX4DcPe1qX21\nSgUHnQ6Gmhqb6mk0GihCQnHcignJjHo9Tm/caH7sGRwEd4USaDTYdEwiImtJPXri6K4EpP3QdEkr\nYTIha/2PyFr/I4a8vRD+9/8fgAvrSCutXCta62L7rNiaTp2gbGyECI/A4Tf+0mp5fWUlMi+J3/eD\n96G2+ahkb5hEk/UkCfqYHtg4bdqFscYt8OzRA169eyEvfiP8x9wFOLU8lu5yIj8Pg1+Zjdy9+2Cs\nq0PR0aMoOHjwinW0Hh5wVjlA2dkD57bvtNgnOzig6+9/B5eeYZBkGdXp2Ti9Zr3Fr4b5SUmI6dUD\nYBJNRO1AdnJCmaMOCfPmt1xIkhBw223QuDih/MBOdB40HFBpAFRYdQyH8nKMeOcdlGZkQF9VhbO7\ndrW6xnPgiBFQlxZDuLqi8PBhi30qZ2cETfw9HIO7wKQ3oPxgCs78stni6nlJRiY8PNxhbMMQHCKi\n1igCApB29HizCbS5jFqNoDFjUHPuDOpTDkK35HPUvvQnq4+h02ow8LXX0FBRgYbz53H6119R18oy\nfbGTJ0PKSIMhLAJ1paUW+5z8/BD44DiovT3QWFWDws27UXQg0aJMXWUVk+ibAJNospoiJBQHV61q\nMYEOu+8+ePXqheKjR3Hyp5/QWFuLwx/9C90n3A+fO++BXFwE05kzzdaVfXzRGBSCcxkZSHl1DurK\nyqBUq+E3aBCGzJuHsvR0pH75ZbN1+02fBqSnQnh6mcekqJyd0WP+83AYGoKs6PM4o7lwIupiGIg+\nz48DDpzFsdeXoK64+EIdmXPsEVE7iYxGwl/mNrtLdnBAj8mTofP1xZkdO5C+Zg1Sv/oKjt7eiHvs\nEbgPHAxFRjpMZaXN1+/WDQ2dPJG5fz8y//tfGKqroXJxQdCYMej++OPI3bYNub9NFHa5Xg8+COOh\n/ZD79IMwXrix0KlrIGLmPwtjnAcyo0pQLVdBEjp41ozDwJSHUbclBcfeeg/GhgYYDXpAVlyTl4iI\n6HL6LgE4Mn9Bs/s07u7oMWUKFGo1Tv3yC87s2IETq1fDPSwcPf94H1y0ashZWRC1zSy7J0lQ9OyF\naoUSh7/7Hmf37oVRr4ejhweiHnoIGnd3pK9Zg9LUpqsNKLVa+EZGwLQ3AaaG/12Q8RrUD91emozq\nnlpkBRWiTq6DUnjC/9kn0e34TJSu2YH0f60AAAgOgbkpMIkmq+k9PM2ztl5uwOzZOLd3LxLmzrXY\nbpbZSVUAACAASURBVKipwb4l7wEA4p58EqHRMRAnLJePksPCkVtcgv3Tpllc5TBUVyNrwwZkbdgA\n7z59MGTuXOyZP9+iTOT48fBxdYHp3BnIWi3cu3ZFmbc3+n2/BHsHnUKdnG5xrGJ1A4p7l0DVS4XB\ncUtx9OHX4ezfBXJdHcemENG1J8uoatCjrri4yS6loyOG/OUvOLJ8eZOrxvqqKmx9cx5kBweMmDsX\nHo6OMJ21/BFS6tMPRzbHI32d5a3YhpoapKy4cLIWNn48ej7xBI5+8olFmWGvzYH2XC5MQkCqr4OT\nnx8Ubk4I/fh57OueBqN0SdIuAWed83F2YD5c+rpjaP/l2PvHp+HqHwBTtXVXyomIbCG7uuJsalqz\nQ/Sc/f0R9/TT2PfXv6KhwrIPKkw8hM2Jh6B2c8OYBW9Dm5UJU3n5/wooFJCGDMO2xUtQdPSoRd2K\n6mokLVsGSaFA3MyZcOrSBTnx8f+LycEBd/7j71AePwYTAKUsQ1YqEfLY/dC8Pga7gk5aTAVkkAzI\n9MxF5kjAr08sBg3ohb2TX4BKo7kmrxF1LF5+I6tVtXDLXuzMmTi9aRPO7tzZ7P6Lkj7+GOlHjkEO\n7WbeJgcEIjsvH/sWL7FIji9XmJiIE19+if4vvQTgwi+QQ199FT3/n737Do/rqhM+/j33Th/NjLqs\nYrnIPe7dcWLH6aQnQOLNhqUFssASykLICwsJ8L6wENoGCOyy7LKQpQRIQnAakB6nunfH3ZZlWX00\nmn7L+4dsWbJG0owsF8m/z/PwPPHMOecemeTo/O4553eWXoh1LCg3GhoYd9FSFjzyPV6+cAdxLcPb\nx2NSKsVL87Yw83//L9Pf+17SB/b323chhBgMvaiIQ2vXZPxuyZe+xNvf/W6/266tdJrnv/QlGl0e\ntKLirs/VjJm8/ehjvQLok+169FEitbVMfs97AAhWV3PV979Hua5h1dV1PmP3Lub940ep+dlnWT19\nK6bq+5ViuyPCa5cdYPHvHqS0Zjx2PN7v84UQYjBUZRU7V/VOFOsKBJj7yU/yype/3CuA7i7Z1sZT\nn/ksianTUe4Tm6fV4gv5y/1f7RVAd2ebJmsffJDCyZMpnTMHgNI5c7j+pz/Bv3sXVqQds7GOMl+C\nhd+4H+dXLmfjuN395tKtCzaw/b0mi37yTfK8EkSPBBJEi+wohZnh/k9PYSGaw0HD+vVZNbPpV78i\nFiro+nOyvIK3fvijrOq2vvMO6ViM6376U67/xv+lIhHF2tozAZlvQgUbFtdhqIHvKrWVzZvzduNf\nMBH6CeCFEGKwNLeHeEtrr88rliyh9pVXiDU0ZNXOi/fdhzlxMgDK6aQ1mWL/c89lVXffs89SddFF\nXPujH3H15/6Z4K6dPVa17USC4JXzeGP6jqwuVEhoCXZfHMMT8mb1fCGEyJXtdJKKRHp9PvX221n7\n4IP95uY5zkyleOGrX0VNmw50Xs36zqurCe/fn1Uf1v/4x8y/+25u/MlDrLh9Jc43XsNqOzGeay31\nFN++gi1j9mbVXrOvFfOmGvQjh7MqL85tsp1bZMe20R29/3WZcttt7HjkkZya2v7Uk8xfvBDiCXau\nfi2nult+8QtqvvMA9ltvZbyOqmOURour94S1L3EtTqQgRZ6mSSAthBhyVjKBtyC/1+djr7yS17/+\n9azbsU2To/v3UeH3w/ga1v37z3Lqx7aHH2bZre/B3NH7jJ9yu2kKhvtdgT7ZIf9h4jWLZBIhhiWj\nPnN+lnNJttmlRyplGLgCAaL13ZK+KkVeeTkdh7MPQiO1tURt8AJmzQS2Ppjdws1xDW+/zXifu0cC\nRbOxDts00ZcuZ3dh5qsB+7KxZDtjZlZjrnkd6HxvqR/7/7r7v5fdPxfnJvn9J7IWKCrq9Zm3qIjo\nkSM5tbP3mWeZfeONaMC27/0gp7qpSIRIRwd5Gb5zlJSyvzz3LLFbK2pZWjMeY9funOsKIUR/zOZm\nRs9fwJb/PZEYUXe7MWKxfo+wZLLxl7+i8p7PEdd0WnbsyKnukTVrSH7ogxl/6WvzZ7C+bFdO7aHg\naHmKKq9XtnSLYed/Lvr5wIXOkpuuyyd077vPdjfOOru2lknXXsObP/i3rs9KZ8/myFtv5dzWruee\nY96sGTSHwxg5jlebfvUrxj/wbXjrjRMfmiY1n7yDA1Nms93z3zm1l9ASxC6bR2D+bRRE93D085/H\n7BY8ux56tMfnEkifuySIFllzNTZQfcklHHzxxa7PrAxbvAdiWxbpZBLd4ch41/RAUn3UUcEALf7c\n7kgFCLs6sItDkOMcUgghBmRZBDwuvMXFXdemeAoKiGVINDaQjro6bI+XdHvvLY7ZSEUikGkFzreQ\nDkdHzu2F3R1UxiMY9bm9SAVZZRNC9M8Kt1Gx+EJQqiu5mL+sjI5juRxy0XbwIPbSJSQ6cp8jpiIR\nTDKfdDG0FJbKfRdjSuucx7b6ayh74AHyDm4EoKN6Fq3dPm84lgdInJskiBZZM/fuYe773kft6tUn\nzqIMMk2/UgqlD+5qFOXKfLueMi10K/dj/hoaGJKbWwhxmuzYztLPfY6/3Xsv0JksTMtwPGYgmsMB\nttUjSU4ulNtN1bLpNKw86b5qTzvK/kVW56G7c/hCOO/7EZqdW8XUx2/J7UFCiPOS63Ats97/D2z8\nRedtA5ZhDG7sdDpRpok2yKzYfc1XVY5jXxf7xFy11V9D69SazMWgxyr1cbI6fW6QIFpkxaw/hA1o\nLz7LVQ98i7/ccy9mKoU+iAHJFQjgcjjQCovwlZURO3o0p/rugoKM55mseISK5snsr8ytPxVtBZgb\nXx4WZ6SEEMOPFYlQWBBj6RfuYfW3vk2itRX/qFE5t1M8YwYqHMY9ZlyP1ZlsaE4n7vJy2Lev13e6\n4WGUUc4BV+/v+u1PugxTrjsVQpwm5qGDTJo1k+TNN7Pjscdo272bquXLqV+T+caDvlTMmYPV3Ix3\n0pSc+xAcMwZHMMjxfZfH58MATtOLx/KS0HLbIu6z/AOWOXmV+rg9P3w4p2eJ00eCaJG1sgceoNVf\nQxCLG6bNZu9jf6Bp61ZK58zJOjs3wMwPfQBHUwNpl4spt97Kuh/+MOu6ofHjcRQU4nro0YzfV3jr\nwd6c04pKjTYP7d6bcWVfRQghcmIdPEBlZRXX//QnbHviCYxEAlcgkDH7bF9mf/ADWO/swCotY8xl\nl/W4v3QgNddeix0s7L0KDZiGkxmxuTkF0Q7bQWGqjJyXr4UQIgf25k3MWLKIMUuXsvE3vyF//Pic\n2xiz7CLMl17E4fXiLy/PKZfP5Pe+F7Oj87jL8QC67IEHOOSvQUvYzI0v5DX/S1m3V2QU408Wk80m\n8Eyr1FXLNlD78pYen8nxmLNDgmiRMxsN56S5TL1nFsbhPXS8s5VnPvTBrOpqTie+9yxm10SdqnAZ\nhcrA4fNhxPq+07m7abffTqypmb7Wv93xIib6prDLk13SnVHpCvzJkqwGMyGEOBXW4Vrch2uZv3gh\n1rgJaB/7GG9++9tZ1fWVlhJZUUrshgqKWkNMSNycfRCtFKMWLCAVi+PMXIBgqoSgEaTd0Z5Vk7Pj\n83DEC2XsFEKcdvaO7QQ0jeUrb8WsrKJ6xQoOvvBCVnWrLl/BvksdFC++EW+jg1kf/SivffWrWdV1\nBQLoLhdWOo11LICu+eQdHPJ3BraWrahMjkX3rcbM4mpVgIWxpVjJwV8P2LDya7hWnvizHI85eySI\nFoNmKR2tahKh4nIW3vtF3vrXb/RbXmkaS37xXdYuPkS7o503S2FasIYrn3iYZ95124B3/k259VYa\nNmxg4uSpfffJcDC/42JaHc00OfpP3BMwAyxvvworKZfeCyHOnNQbr8Abr1B98QqOXnop+59/vt/y\nrkCABb/7Li9P3Yah0mhVGosCM7jkPx7kxY/ePeDzFn3hC2x7+GEu+8KnKYjuodXf+/ydHQ9wVeQm\nngg9QlJL9Nve6NQYJnXMxhpEDorjMp3zE0KIPlkWyReepWrZdBZ/4wHCd9xGeM+efqsEJ4yn+rsf\n4ZWyzh2K7rFulhcuZMaRO9n8H//Zb13d42HJl77EmgcfZPRXvozFsQB6as+g1dlRwrv0G3gq+PiA\nScbmxhZS0FGNLTt4RgQJosUpsz0Bxr3/LgKjR/Pa/V8hniHrbP6kScz4zj1surLlxEqHgm2j9pDS\nR3P147/mrXvup2nr1l51PQUFTP/gBwnv28fBF19k7pf6e4OoIJrPFdzEprw32e7e0mtQU7ZiQnIS\n86JLUR1FyHZEIcSZNv6Td2AD+fM/yeZ5s1n70L+TjvbOHFu+bCmTvvEJXluyG0N1vmi0lMXr4zay\n6MqZXPrTH/HW17+Z8d7UYHU1sz76UXb+8Y/objf2nv0467dChi3doHC2l3GjfRtv5L3EQef+XkOj\nbjuYHZ/H5I7Z2PHgoH9210OPMnp75iM5QgjRn84jKTZXPfw7Nv3g2+z8wx+xzZ7JYZWuM/62myi9\n992snr6tayxLakn+OuE1rvznm/EWFLHhJz8h1d57903JrFlc8Pd/z9vf/z6Trr0WbW/fwbptawTD\n47nRvpVX8v5Gk6OpVxmP5WVJ9GLKOyZjpwe/Ci3OLRJEiyFhe0OUXLeS2woc1B8JU39wH9FIK57y\nYgIXTad1hs6rFQe7JoHd7S45xKiyPIqnT2fye99LR10d8eZmHF4vobFjSXd0sON3v6Pj8GHm/fPn\n0CvGYfWbzEZBtIDZ8Su4wDeXJlc9zY4GAAqMIkrS5bjiRViGAwmghRBnQ0f1rK4V4bETl1NZVUU4\nmeboob0kE1F84yrxXTiFwzMSvFCwFVv1HvTerN7E8nETqLn2WvIqKmjbt490JIIrGCQ0diztBw/y\n5re+RSoS4coHvo3x2guwaGKffbJR6JFSLo7fRNLbzFHXYcJ6Kzo6xekyClNlnVu4T2EF+riTV3NE\nbvrejyXEyNRz94qC8hou/sr/YepFS2kJh2k4tBdLWQSm1eBcNI49F7Tyjm9Lr2merWxenLCW+ROL\nmfWRj+DweAjv34+RSOArKcE/ahQNGzfy8pe+hJVOU71gPqk/PUJ/007b1vCER3NVYiUxdzNHXIeI\nah24bDdl6QpCyRK0RAhrsNm8xTlJgmgxZCxbkXdwMwFvKdu+OZM2V4SkShBT+weMVXdPbSLosFh9\n//14i4vxFBSQjsWIHT3adRd1qKaGmvf8HZad3QTOsnT0jlJGUUKFAhRYVudEUc7xCSHOlpMnYxYa\n5rOryP/cx3j7Mzq2sompOEltgMvrFbROgUN/+QvtBw6QV16O0+8n1dHRI3HO+CuuIKRpGEY2Z/YU\nluHCGSlnNKMYo3V2uDMLt4ydQoiz43hCr9auTxRh/zhKL7WwYgdYO6fzaF5Mi5JW/efFSWoJmDWW\ntR/7ItB5/7Tu8ZBsbSXe3NxVbuEnP4lj8zrKl03HPWv2AC//FFbShyfpo0ZVdV2gYMnYOWJJEH0O\n6J4u/1yW6RzdydyzZpMIednn3ZxT2/XeRmrecwl7/vf3xJuaiDf13A5TMmsWK378H1Cee1ZGG9U5\nARwOf8lCiHNeq78GG07pWryTx9OqZdN5p1yjxdGSUzs7Rh9k9t3/wNrPfo2Ourpe30+69lpmLL+I\n9HNPAZ1jdLZsFKbM/IQQp9HxFebjUzRF3/cgZ5yHajqHKmOE9bacnrvvgjCjr7qMA6ueIVJb2/NL\npVj0qU9RXVaM8crzuK/tfRa6P5atZM55HpAg+hyRKVnBcHRo6i0Yob1AbkE0CgoWzWTBPV9gy3/9\nvGs7d/WllzHrC59FTSwk7tJQ9mHcpg9HvABTtsUIIc6Svq7ZG6yGlV8lWbgp53oplWL0DTcQ37Cf\n3Y89RqqjA1cgwNS/v4OJd70Po8JPyqHQPnwDnnSQ2kTekPZbCCEGy2ysO5HxeuotjN7+KHt++HBO\nLygj/jG0Fq7L+dkRdwcXf/EzkDA5vPpVzGQSb3Ex8+66k7Jrl5FyxIil4ugXfohosJjOqFjmneIE\nCaLF0BtkcKs8AWo+/gXG/90HwEhh5+tEimO84VtHrfNg19jlsl3MiS2gOlmDs6MUO8vt3UIIcS5T\nDG4s00PFzLz/O8z41D3YpoFd5qA5v42/+l6jxXFia2KeFWB+dDFlyWq0aCEyIRTi3GU21qGXVJyR\n53BSYq4zqWrZ9K5FpENTb8H1UO4LStogwhnN1ghUlrPkx/+NHWlFYWNUaBwJ1PNn3xvEtBNXr5YY\npcyNhSiIV6ASQWTsFCBBtDgNvJYv5xd2ylZ4LB8WGhSUgzfMmuBLGe97TqkUb/pXs8b3Ju/SbyQU\nHpf1OWkhhDg3KXymP+daeVYeDtONpbmgqBI7r5m/hVbRdCyZYncdWoQXA3/F7/Nzjf5unO1lctWK\nEOeYx1e18dV/uoO9P3r4zDzQNBn/yTvOzLMyONVdmKYNo9IVvOPu/xz0yUqjBThTEHXnodx+EsE6\nngw9SkKL9yrb6Gjg2eCfKfdUcEn7NRAt4FwKpE/laNFxStfPyEubkUSCaDHk3IlCalKT2ON+J+s6\nE5OTcSUKsADljLMu+GrGALo7Uxk8FXyMm6yVuNsrAdB1E8uRQqFQhgvT1DiXBjohhOhLIFmM38oj\nqnVkXWd+dAkkO7do274wfws9kfGKle6iepQnQ3/geus2tI4SwEZ3GNh6CmwNZbgxhyADtxBicNZX\n3UyI0xtEdz+L3P22gOHGthVlqdEoW2W8xaAvU62FtDk7z14bgQZWhf5AUkv2W+eIq44Xg09ziXk9\nJIIoZaM5UliagWbr2Gn3Gc/APRRHi0p/+xUOr94+BL05v0gQLYacaTiYHp/DHtc72cWvNkyLzz52\n5RTEvI3s8PS+LzoTS1m8kfcyK8x3EXY2s8WzgVZHM6AoNUqZEp9JIFmCnchDgmkhxDktGWBRdCnP\nB57NqrjDdlKaqsK2FQqbJvfhAQPo42JajP3enUywnTS66tjiXUeHFkVHZ3RqDDWJKfgSxVgpDzJ2\nCjEyHV+BPjRMA+jjXPFCJvumssOzLavyhUYheckibBSaZrHVu2nAAPq4I67DhD1HyHMkqHcfYrtn\nE0mVxImLSYlpVCbH4o4XYhmuU/mRxDAgQbQ4DRR5HeVc7FjBK3kv9F/UhuXRy/FFyzvvKNVMdnty\nextW56il1rubl/w9nxXW29jlfoeQEeKKyA242kfJ1kUhxDnLthWjohOY7DzEzgEmg5qtcU37jTij\nxZ07eNwx1vpez+l5671rUAre8L7W4/NWbwubvOupSo3hovbLUXJ+WogRaTivQHdnGQ7mdCylydFI\nk6Ox37Iey8sV7dcfW1yBtK+FrZ7ckjqu871JwAqy66Qdl6/7Xwbfy8zyzmVa+0JIBHP7QcSwIvu1\nxGlhmS5Gh2dyZft1+CxfxjJ+y8/VkeupCl+AbXa+z7HcEbZnuQrdRUFCS6D1cS467AjzRP7vSAfr\nkTsHhBDnMjvpZ17bJSyJXozTdmYsU2gUcWP4VgLhscfuIIW4K5zz9Vgp1Xn0pS+1rgP8Jf9x8Lf2\nWUYIIc4+BdF8rmi7iWmJGahMW6ptqE6N4ca229AjJRx/MdjhaMFUuSVWq3fWU2yV9NmVjd51rA29\nCO7sj+acbbZpYtQf6kw0J7IiK9HitLENNyVtU7kxUUm7u4lDrr0ktDhey8fo1Fj8tp92FaY2tBWn\n5SRgFuCx3ZjKyPlZSZXAZbtIqETG71MqxYuBZ7kyeRtW0nuqP5oQQpw2djKP8elFjIlPptl1hCPO\nWlJaiqAZpCo9BrftokW10FK4AbflIWgUYqn0YJ/W77ctjmZ2+DdwQfwSOSctxBnWV8KoU0kCdfK9\nzCNHZyA9N3EF073zOOqqpdFZj4VFoVlMeboSUDQ6jmAV1uKxfISMYoxBzDk79f83+I5nB+N8EylM\nTuFc38nTsPJruFbK2ehcSRAtTivLVhAPEYwHmamNR1M2KX8jb/heYb9rb69xZXl0Rc7JIQCctov0\nAJPIRkcDMXcTnuToXH8MIYQ4oyxLQ0ULKY4WUKZNQ9MNIr46XvW/QKPzpMzbNlwau/y09WWzZwOT\nvDPRosWn7RlCiBMeX9XGlLsz50a4pmITzfd+ftBtH7+XGYb/WejeFKbpQO8opZISqjVQzgRHfft4\nNriKDi3So7Ru61wcW37aerPG9zpXx8ZgJTPvyBTDmwTR4gxR2LZNOHCQVaFH+1xt3uvaQ3V6DAdc\n+3Nq3WN7stqOs9+ziws6qjCtc/utoBBCdFJYKs3hwFZeyPtL5gUNBa16K37LT1SLZt2ybutZ3U1t\nKIOwq4mCaBHn+oqKECPFjnW1fXwzk0WDbPP4KvSpXis1HNgoTEeMzaFX2ezdkLGMqUwMZeS8eJNv\nFhA5KSDPpNnRRMzdjEeC6BFJ9maJMybtb+Cp0GP9btc+5DjIaKM6p3YLzUJa9OasyjbpDWg5rnIL\nIcTZYxPNO9x3AH3Mdtc2pqam5dTyxNQkdjl3ZlU2qkUkfBZimLMZmiuRhgNN2dQFdvYZQB+337mP\nsca4nNqekprCTld291In+zhmKIY/CaLFGaEpm0OefQNuuUZBm95KmTEqu4ZtmJGcmfVg1rnqIlNB\nIcTwoDlSrPO9OeCwldDiOG1Xn4kcT+awHZSZo2gcIJPtcQptBJ6hFEKMVKY3zBu+VwcsV+s4xNj0\nuD6T054sYAaxsAaez3aROedIJUG0OCMsb5h13reyKrvFtZmJqYkUGQOcv7NhUWIJO1w7sk4MUZ6u\nxLSyKiqEEGddwtvMIeeBrMq+7XmLRYkleK3+kyc6bAcXxZexxpPdmAyQZ8pVLUKI4cKm3dVIQosP\nXFTBWvcaLoovGzCQ9pt5zE8uYK1nTbbdwJvli00x/MiZaJGRrlmgpwEbZbkwTMWpvE1L6FGSWpZb\nWhS86n2FBcmFTE1OY713bc9zfjaMS49nZmI2r/tW0+A4ml27NlQlx3UmOxNCiCFno+s2aClAgek8\n5YzWUT2S9dBrqDSvel9mcXwJcZVgo2d9j9US3daZmphOTbqGv+U9m/X5abflJpgqRlZUhBCnh41D\nt7D0NMrWwHBinsJcTVPQ7GgYuOAx7XqYDe51LI+toN5Rz07Xdix1YsXFbbmZHZ9PgR3ir/6/ZH0l\nVmV6NJ5EEbJ2MzJJEC26sdHcCaLuRvZ6dnDEeRhL2YSMAqbFZxJIlqASAexBTKRsleMQojpXVa5t\nu4UbonfQ4WwlrSXRbA2fFcAdL8TW01i+7O/2G5MehzdRRG63AQohRP8UNng6iLgb2e7dRKujBWUr\nyoxyJsan4U8WH7tabxBjZ47Tr7RK84rvZd7T8j4mdMwk4mzFVAaarRMwQzhjhaS9raRUKus258UX\nocfzZSIohBhSmrIxvW20uo6yzbuRqN6Bw9YZm6phdKIGT7wIy3APqu10DmMcQLvezkveF/i75juZ\nri0gqrdjKROH7SQvnY8jXkAscBiT7K/EmhNfgJV25dp1MUxIEC2OsbHymngh+DT1zvoe3zTrTex1\n78Jv+bmy/Xp87VVYWZ4dOc5hOzszWuQ4h9TQIR4iLx7q8bkFYDi5rP06nsj/HfEBtuwEzCBLIisw\nZTATQgwhpSwSgcP8JdT7+pQmRyNbPZsoMUq5tP0a9EhJzi8hHfYgxiwblK2jYoUEKezxlQk4YkW8\ny3ETq0J/7LHaksmY1DjGdEyTHTxCiCGl9BRNwT28EHi210u9BkcDb3lfZ6JvMvMjyyCWTy4TSNuG\nPCv3IyhefNiWjiNWSojSHt+ZgCdaxiXOK3jR/9cBuzM/tphgtGpQC09ieJAz0eclm56XxNvYeU2s\nyv99rwC6u6gW5U+hR4gGD3WuvGT1nE6eRD4VRmVOvfRaPvJShf2UUOiREm5ou43KdFXme+9tGJes\n4dq296I65HoWIcSp6DnIKGwSwcP8Kf+RXgF0d42OBv6c/whmXmOvNgZ6TiBdgNvKbSVmTHocrkR+\nn99btiLQPoYbw7eSb2Qup9kac+LzubDtSkgEcnq+EEL0dNLYqZk0BnfxbODPfe+KUbDLs5MX8p8E\nb3uWz7CP/ZOiJF2e3XDbzaz4XFQyr+8nmE4qwhdwVeS6PnNPuGwXyzsuZ1LbAuy0J7cOiGFFVqLP\nE8e3zERczV3p9r2Wj7xkMQ7Dw+q8l4nqHQO2YymLZ4JPcHP6fcfeDHZnozlTxL1NRPQ2LGXhsB2E\n0kXo8QJmRxdSl/9Y1n2eF1uIlgj2u4XQRqFFSrgkcQtxbzO17n006Q0ooNSooDI5Bk+8CNNwIgG0\nECJXmmZh+FoIO5oxVAqFht8M4E8WY2tpng0+kdX5uJgW47XA8yxL3oyV7hkUK2xwR4m6m4npEWxs\nnLabYLoIV7yA2fEFvOkfOMvscTNic7EMZ79lLFvDGx7NNYnbibqb2e/ZRVhvxWm7qEyNoTRViStW\niGnpWT9XCHHmGMfufM7k7M92bDSHQdLbTLujBVOZ6LZO0CjAHS8i5W3hb4GnsupovbOOff6tTEgs\n6XVOWlM2lqediKupK4mYx/ISSBXjjRdR5R9Nravvv6eTukxlcuyAu25sw0Vx21RuSlTS7m5gr3sX\nMS2Kx/IyNjmBglQZjnj+KZ3pFsODBNEjng3edvb6t7PO+1av5F55Vh7z44vw2v1nc+0uocVpcx0l\nPxbi+AioNJN4oJa3fK9x2Hmox8CobMWk5BRmJuYyJTGNHZ5tAz6j0CiiKj4hyy2ECivtwZ2uZKKq\nYIrW+WObdueKi5yBFkLkrvOIy1bfBrZ6NvUKlIuNEhbEF1FgFmadoOuQ8yAJbzOudEXXZ8oZpzlv\nP2/6XqXN0dqjvMN2MjM+h4mpybyT3kars2XAZ0xMTCGQKCOb2amNgmQevqSfGR3VaKpzG6RpdX4n\nY6cQ554d62qZ8q9/7LdM6N53A1DzyTt6fZdlSDloSlkkA/Vs8L3JHteunkORDWPyxjEjOZOg1wUF\nbgAAIABJREFUFSSsh7Nqc733bcZ6p6JiBSca8kQ46H+Htb43iGmxHuV9lo95scUsjV3Co47fktYG\nvo5qaXQ5zlhRVovXlq0gHiIYDzJfm4A6NnYaFiBj53lDgugRzQZfGy/kP0m9sy5jiQ6tgxf9z3Vm\nu07OYpN7Y1Ytb/Kt5ZKOcViGC6WZNIV28JfAkxnnbbay2enZzl73bq5vvxlQ7PBs7bPtEqOUy8LX\ndRsss2fZCktGLyHEKbExAw08mf+HPgPkJkcjTwdWMScxDwUccmYxNVVw2LWfGlWOZSuUM87O/DdZ\n68t81ZSh0qzzvcUe106u7rie5/zP0ORs6rP5SYmpzGtfBv1sR+yrY6aFTPyEGCYeX9XW53eOUBF/\nf+yfD0295cx06BhN2XSEDrAq+GjmHToKDrj2cch5gIviy9jgXk97FoF0UkvS7moidHxe6G3ntfxn\nOeDan7F8TIvxSt7zjEmN4+b29/Kn4KN93xBjw5LYMsa0z8Q2+9/Bk+kHMiTj4nlLgugRTHOmeCP4\nUp8BdHf7nHuZmZxFsVFMk6PvSdpxrXoLtiMJhpN4Xh1/zWJbTlqleTLwJ24M38bkxAVs92xil3sn\ntrLBhtHpambGFxBMlBw7gydbYYQQZ4E3zLPBx7NaYV7vWcvFseUccdRjqIFXOxodDUxUYGPTENjd\nZwDdXdgR5vm8Z7kyfANtjlY2+N6mzlELqnOnz5TkNCbHp+NLlGCn5E5SIcTZkco7ypPBxwY84mIp\ni1e9L7MsvpwXfS9k1XZCixE6tk18Y/CNPgPo7g649hE0Q7y75Q4aXXW87Xu9a8eP03YyKz6XMcmJ\nuKPF2KYknhW5kSB6BIt7m9jjeifr8ltdW1iUWJxVEK2OBbiaw2Cj7+3OQDgLSS3JEWct1S1zWRCt\nZJ774q7rVxwp/7Hs2RI8CyHOFps291HCjuy2GQJscW9iSmoKW9ybByx7fHSzPe287n8562c0OhqJ\n6O2E2mpYER2N6YphKRPNcqAnA1imLllghRBnjaYs9nvewVDZXQFlKYsjjiOUGqU05HCnc8rbwlb3\npqzLb/FsZEp8FiWtF3BNbBymI4GNjW450JJBLEvLNf+YEIBk5x6xNM3igHt3TvGoqUxMTFxZXKlS\nZBSjGW6S3mb2unbn1Le1vjewPJHOxDfRAvSOElS0EDPtRgJoIcTZpLkSrPe9mVOdVr2VAjO74yel\nRjmWbRNxNWV9lvq4Ld71aA4DK+1BRQs7x85YAZbpQMZOIcTZZHjb2OBdm1Od3c5dTEhPzKqs1/Kj\nKahz7c964QY6jxQecR3oPLec9KNFi9CjxRDPx7IkDBKDJ//2jFSOFPvduQW3AI16I/lZTAZnxOdh\nGk6ienvOc7eYFiPpyG3yKIQQZ4LpjPd71V9f0io98HUqNlQkx2DbimZnY87POOQ6gO2M51xPCCFO\nt4Sjo3MczIGpTOws1oE9lpdgshhds9jn2ZVz3/a5d6Fpst4shpYE0SOVsjDIbktNd4YycNr97/L3\nW36CyRJAYfV7AVXf7EHWE0KI02mwY5qpTBwDnJAal6rBEy9EKUj3dTdqPwwMbCVjpxDi3DPYsTMb\n82OL0BIhLM0c9NzWzuIqQiFyIWeiRyhlOvBa3qyvDzjOa3tp0vteIXHYTq4O34iKB7HpTMyQMxv0\nwdQTQojTTLd1dFvP6u7n7hy2s9/JXcAMsqjjEkzDiQL8Vq4ZtDvHZ2U55PyeEOcxR6iI6y7ub3wy\naV/2KMWBM9YloHMMHJz+tzOOSY2jOjoVy1Y4LAc+Kw/I/gw1gM/yo2y5814MLVmJHqEsw8n0xNyc\n61Wmq2hWzRm/KzSKuKntVjyRyq4ENnnpQtyWO8dnjMadzM+5b0IIcbrpiRDTEjNyqqNshcf2ZJ4L\n2p2TwGvb3oPqKAQUNori9KiBt3+fZGZiDlrO11cJIUYapRQlwb7/d6YDaABvqoBCoyinOnlWHmYf\nLx+VrZgZn8OFbVdiJzrHPcNUTI3nNj4DTI3PxDAlb4QYWufMSrRRf7qvfz+/2CiKkqNw5DmyzpQY\nsAKYmFycWE671k6L3oyFRdAKMSV+Ab5EKVbSi9VtpuiI5zM7voA3/a9m3bdZ8flYablKQIihcNN1\n8kJqKJmWxoTEVDZ7N2RdZ1x6PAniXBxbTrPeRFgLo6EoMcoYn5iCO150bMw7MXZ64kVU+UdT68ry\nd58NoxM1mLZMBIU4nxnhZmw7n8b2/suVBM/wWJH0Mi+2mL8Gn8y6ytTkBdjAxbFlHHEcIa5i6Dio\nTo2hIjEeZ7zwWOLE4xShVCluy9P3vc8n8VgeQqlSJPmiGGrnTBDteujRs92Fs+p0vEJwxApZ7r6c\n5wLPDFhW2Yq5ifm84X2NpJYkzwoQMkNoaBgY5EUrMYzeW3VMWzE2Ppntnk206wOM6MDY5HhC8VHI\nYCbE0DjjE6XzgDdewkzPHDZ51w9Y1m25qTbG8KL3eVBQYBaSZ/mxsMmzArgj5VgZAl8r7WJxdDmP\nO3+b1YvOhbELccWKZCu3EILHV7X1+/3ZeLlqoyhOVFHuqeCIq27A8sdXrd/2vomyFSVmCR7bg6EM\nQuki9I7SjKes9Xg+l3ZcxdOBPw08lbRhRcdVaPGQZOIRQ062c49glq1RlqxiSezCfrcN6rbOithl\nRFWUpJYEoEOLcNhZyyHnQUqMspPeBPakRQt5V/jd5Bv9Z/Uen5zAkvbLIXkW9hkJIUSW7LSbaYkZ\nTElO7bec1/JxZfQq9jn3dk3mWvUWDjkPcdhZS8AowOpz7FW4I+VcH37PgNcKzo8tpqZ9DrYpO3iE\nEOculQhwUXw5o9Kj+i1XbBSzLL6cHa5tQOc1VA2OBg46D1KnH8bbT84Iy1YUtI/nisg1qH525ihb\ncUXkWgrax2d8kSnEqTpnVqLF0NOcSdbkvUaLo5GL48sI62G2u7Z1XUHgtbxMTV2Az/LxtuctatI1\nFJpFtOjdzkTbUJkcO8AApNAixbzLuJVWdz3rfG/Q4DzaVX9cagIz4nPIS5RiJ/2n7wcWQoih4G3n\n2cCfybcLuDi2nHrHEfY4d2Mdy4ydb+YzJTUVUDzjf5oLE0s57KjtsaLss3yEkiVd+SMysWyFNzya\nm407aHDXssb3BpFjO3p0W+eCxExqElPwxkux07nlnhBCiDMtndfI44E/MCU9lQmxSexz7uWwo7bz\nJaMNo8xyatITiKoO/uz/ExcllvGS74UebYxL1XQegennObbppDQ8lXcbRdS697He+3bXIpDbcjM3\nvpDK5Dhc0VJsuQtanCYSRI9gCU8zO93bQEGjo5GQmc/sxJyua1iSKsVO13aiWuedzVu0zSxILOJ1\n7+quNqYnZ+EaYDDrpCAeoiAe5IpYNWlnFEuZ6LYDRzKIZTj6nUwKIcS5wSbsPkqro5VWWtnn2Mso\ncxQLE4tQKGw6d+qs9azpeiG5xb2ZSakpbHNv6WrlwuhyVCKYxdXRCi1aTHm0iOtjEzAccWwsdMuJ\nnsjHsjTZwi2EOOfpymaPezdpLc1m9yaUrRibHseSxFI6t0MqGvUGXves7noh2aAfpdgopsnRBHSu\nHs+OL8TKcHzwZJal44yUU9MxinGeCzD0ziDaYbrREkEsW8nYKU4rCaJHKE1ZHPLs6XFeJKy38bb3\nrT7rGMrAxsJpO0mrNGNT45nRvjirwewEhZXyoqe8HL9MQM6hCCGGC82ZZL2v2zipoN5RT72jvs86\nzXoz05LTu/48P7aY0siEnLYQ2ihI5KFzYhujjJ1CiOHC8Laxwfd2159tZbPPtZd97O2zzjuunSxM\nLKbJ0YSyFVdGrsUXqeiRwHYglt25iNM9oJGxU5wJEkSPVM4Ue9y7cq7WoDdQYVQwLjGJ8tgESMh1\nKkKI84fpinHEMXBSnJOlVYridDELYhdRGK3GSntOQ++EEOLclHBESKlUTnVMZYJtU50ay7zYYnyR\nSixL7nMWw4ME0SOVsjBI51zNxOSS1mtJJ/zYkohBCHGesbAGdXmAy3JzVfPKXtcACiHE+cAc5Ppv\nvlnE+NYFWIZLxk4xrMhp+xFKWQ48tjfnenlWECspAbQQ4vyk2zqanfuvRo/px0p6kev7hBDnI4c9\nuHU5p+HDMtzI2CmGGwmiRygr7WR6fHbO9cYlJmLIYRIhxHlKSwSZkrwgpzrKVoSMImQSKIQ4X3nT\nBeQbud1P7bf8+NNn/k5rIYaCBNEjlI2iOFmBbmd/tsRv5RFIliATQSHE+cqydCbFcwuia1ITcceL\nTlOPhBBiGEj4mBdfklOVebHFqETwNHVIiNNLgugRTI8XcnHHiuwK27AiciUqGTi9nRJCiHOcL17C\nBfGZWZV12k7mxBZhGpJiRAhx/rJRlMRHU2qUZVU+3yigIj5Ojg+KYUuC6BHMtjQqItM6A+l+LsvT\nbI2rItcTah8rg5kQ4rxnp73MbL+QafHp/ZZzWx6ub3sPzsgoZAePEOJ8p+IhLm27jlHp8n7LFRlF\nXBW+CRUrOEM9E2LoyavzEc5OexjdNotb0uXs9exks2cjpjIAcFtuZsfnMzo5Hle0DMuSdypCCAFA\nIshs6xImJKexzbuRPa53sFXn28g8K8D86BLKklXo0aLOO56FEOK8p1DRQlaYNxD2HGW97y2OOE9c\nGVhilDI3upjC5CiIh5CXj2I4kyD6PGCbLtztVVzQUcEUzxzSegplg9PyoMdDmLbqb6FaCCHOS3bK\nhz81hsXRSua6l2JqaZSt4TJ8kMjDRsZOIYToSUEiRCgR5NJYNSlXO5Yy0WwdVyoPKyW3GIiRQYLo\n84hlaahYIa5un5lnrTdCCDEcKEzDiW4UczxNowTOQggxEIWV8uBIebo+kctfzn22aWLWHzrb3RgW\nJIgWQgghhBBCiPNYw8qvUXb9nrPdjWFDgmghhBBCCCGEOM+1+mvOdhdOq1FD2JZkkhJCCCGEEEII\nIbIkQbQQQgghhBBCCJElCaKFEEIIIYQQQogsSRAthBBCCCGEEEJkSYJoIYQQQgghhBAiSxJECyGE\nEEIIIYQQWZIgWgghhBBCCCGEyJLcEy2EEEIIIcQIsaMuD+igJKjOdlfEMDB6+6Ps+eHDZ7sbZ8aT\nzw1ZUxJECyGEEEIIMULsWFfLlIr8s90NMUwkN25A6Tp6ScXZ7sqwItu5hRBCCCGEEEKILEkQLYQQ\nQgghhBBCZEm2cwshhBBCCCFOSTgGKcMe1FnsxnY7q3JKKYoDOTcvxJCTIFoIIYQQQgiRs3DsxD+n\nDJsddXkoFcWpQ8iXudzJ0mZnMrQd62oHfN5N1+UTjp3ehGnd+32uKYjuIe/gxiFtc8/LW1C6PqRt\nng8kiBZCCCGEEOI80hQB2z6x+juY1ePjbXRmA+/UGQhXMaWig8b2nuW7lztZNgH0QG0MhSkVHYRj\n6pwNpI9+/vM0DHGbCiSp2CBIEC2EEEIIIcR5xLZtHl/VBnSu7g7Wqld0jHDPAHjHulp2rMtUum3Q\nz+ne9ulVxczq6Gl+xqnRR40+210QSGIxIYQQQgghhBAiaxJECyGEEEIIcR4Lx/o/tyyE6EmCaCGE\nEEIIIc5Tq17R2XTQT8rILkO2EEKCaCGEEEIIIc5bRrj5DJw1FmJkkSBaCCGEEEIIIYTIkgTRQggh\nhBBCCCFEluSKKyGEEEIIIUaYpggUB3p/1v1+6JM1tve+OzocQ85LC3ESCaKFEEIIIYQYQR5f1Xbs\n/mfV4/Pu90NnqnNc97uj0+bx+6CbT0tfhRiOJIgWQgghhBBiBOq+snwqdSWAFqInCaKFEEIIIYQY\nYfpacT7ddYU4H0gQLYQQw9iprDIMJ0qpXmf7BivXvzOXQxHyDVwu01nDoey3EEIIIc4NEkQLIcQw\ndr6sFmQ62zcYTRHYUZeX9Z2ojlAR1y+zsiqb6azhUPVbCCGEEOcOCaKFEEKcV3bviedU3rZtGttP\n/FkphVMnq9Vp6L3yLavTQgghxPAmQbQQQgjRByPczOOren7mCBVx3cUm2awwZ9opIKvTQgghxPAm\nQbQQQogR7fhZ5R11eQAY4ey2cvelM0tt/oDlhBBCCDEySRAthBBiWAjHst9CfbLOO05PLXjuTzh2\n2poWQgghupiNdUPepl5SMeRtjnTnTBB9qD51trsghDjDpo452z0Qw8WOujymVHT0OJucS92hDqA7\nV7U7Mnw2cKK3THVzkW228HPJ+ZJF/kyZerY7IMQItXtPnCkVdla/a053fotM4+acf7qD1KYNQ/qc\n2pe3YNYfQh81ekjbHenOmSD6meejZ7sLQogz7MpFJWe7C2KY2LGulh3rBlt76DOYZ+5Pds85lZ9l\nytwqZlYPz9+X50sm+TNh2Qw5TiDE6ZApD0YmudzccCpOHjcfZwWwYkif8dl/baHl3s8PaZvnA+1s\nd0AIIYQQQgghhBguJIgWQgghhBBCCCGyJEG0EEIIIYQQQgiRJQmihRBCCCGEEEKILEkQLYQQQggh\nhBBCZEmCaCGEEEIIIYQQIksSRAshhBBCCCGEEFmSIFoIIYQQQgghhMiSBNFCCCGEEEIIIUSWJIgW\nQgghhBBCCCGyJEG0EEIIIYQQQgiRJQmihRBCCCGEEEKILEkQLYQQQgghhBBCZEmCaCGEEEIIIYQQ\nIksSRAshhBBCCCGEEFlynO0OiNNL18CywbbPdk+EEGL4cOhgmGe7F0IIMbycT2Onbds0tg9tmyVB\nNbQNitNGgugRqDgfKvITtDS0EQ4ncOgahSV5ePKC7K13Ek9KRC2EECerKoVCT4yG+jY6Iik8Xgcl\nZQFMPcDeI9p5MzEUQohsaRqMG2XjJkJDfTuJmIE/z0npqHza034O1J/tHp4eRriZx1cNbZs3XZc/\ntA2K00qC6BFE02DWuCTPP7Odr/9mC4lEzxlfRUUeH/unhZRXVLC3Tj9LvRRCiHOLz6OYUhHl1/+z\njmee2dNr5860C4r5p7sX024XcrRFVgmEEAKgMATlvjZ+8sM3Wbu2d7R8yYpqPnjnfPY0BojEzkIH\nhTiN5Ez0CKEUzBmf4F8+/xS/+O+NvQJogLq6Dr78xed5+el11FTIkooQQnhcMLEkzD9++HGefrp3\nAA2wbWsTH79rFfGjBygrkJ08QghRGARPoo6PfOhPGQNogBdfOMhHP/QnKgMt5PnkBaQYWWQleogE\n/YrRxSmMVBLbsnG7nURND16HgZFMYNsWLpeTSNrLwaM9zyg7HTC+3ESzEpiGicOhY+peQKGbMQzD\nRNM1cHjZe0Qnle79/MlVJt/82vMcPDjw4Yzf/24bEycWEiydQHuHTAiFEGdPSb5iVChBKpkCwOl2\nETU8+B0J0skUtg1uj5OGiIejLT3r+r2KsaUpjGSya4xN4MWpGdjpBJZp4XQ6iFte9tcrLKv38ydV\nJPjUx54iGs0wsJ7kq/e9xH/8/AYaVIHkmRBCnFVVpVDgiZNMGiilcLjdJEw3Pj1GMpFGKXC53Rxs\ndhM+aa5XGFRUFiRJJZMAuDxOYoYPr54gnUxi2+ByO2iNeznc2MfzQ+18+P1/G3AsTCZNPv1PT/OT\n/7yZjQd8Q/GjC3FOkCD6FOX5YHxJjE3rDvG5+9bT1tY5IGma4qqrxrF0aSWvvlrLM8/sA2DZ8mpu\nXTkT2xNi/1EH00anaDjcyHe/tpZdu1pRCu66azbl5X7++Md32LTpxOg1vibEhz8yn/KxpWw76MLs\nNiE042G2buljpMvgxz98m+8/NJrNHa6h+YsQQogclOTblPoivPT8Hr76u60kk527YzwenZUrpzJx\nYgG//e0ONm9uxOHQuPmWyVx+9WTC6SBtHRoTy2Ps3FLH//nGWhob4/j9Tu6+ey62Db/61VYOH+7o\netaCheXc/r7ZeIKFvHP4xK89r1uxd+cRwuFk1v3++c/W8Pf/eDn76mRVRQhx5o0pM3GZYR7//Vb+\n+pe9XUFsQYGH979/Ovn5Lh56aAMNDTECARfve/8s5i8aw8FWP7oGFcEOXn91L998eDOxmEFlZR53\n3TWbhoYov/zlVtrbO19oaprimusmcO31U4kT5FDjiWOAlSXwx99vwbKye5sYjxusfesAxROm0RaR\nN5BiZJAg+hQE86DM3czHPvx0r+3TlmXz9NN7efrpvdxyyyRuv30qv/71dl5+6SAvv3SQG26cxAc/\nPIvPfOov7N8XBjq3ZN9331J+//udbN3a1Ot5e/eE+dK9z1FdHeSb37ma9fu8mGbnYPbnx7bl1Pdw\nOElbUyuaVpZxdUYIIU6XimKL1kP7+eLXX+n1XSJh8otfbEEp+Mxn5pOX5+T11+v4/SPb+f0j2/ni\nly5i6rQiPn7X00QinZO9QMDFV75yId/61ps0NcV7tfn2W0d4+60jLL1oNHd+4iI27et8eThuVJqv\nfGdtTn1/84067vzHDiCQ+w8uhBCnYFKlwQtPreeR3/We87W2JvjBD9bg9Tr4ylcu5Mc/Xk9tbYSH\nfvQ2jp+u5d//8xqamxJ85O7nuoLfmpp8PvCB6Xz966+TSvWex656YherntjF+z84iwXLZ7DnWD6d\nQm+Mp1btzqnvv/ivDfzgpzW0RWTxRowMciZ6kHQNxuSHufsTT2U8f9zdo4++g2XZXHRRVddn06YW\ncPcnnukKoAE+/vE5PPLIjowBdHcHD7Zzz2eeYtHkJEG/IuQzeP212px/hp07GvF7M6+mlBQoxldC\nTaVNVZmGkkUXIcQQCOYpUs2H+UaGALo724bvfW8NK1ZUU1mZB3S+aMwPObjrI092BdAA99yzkG98\n442MAXR3q189xH//5FUWTjFxu8BKJzlyJJrzz9Da3HedilJFTaXN+EooLZSBUwgxNKpKLFY/tylj\nAN1dPG5w332rufvueTgcndP8UMhN7YFW7vnc37oCaI9H5yMfmcX996/uFUCf7H/+eyPb1mxnxgSF\nrkFbSzTrVejjIpEUiVjmMVrXYcyozrFzXAXkB2TsFOc+WYkepLHlFg9+/3UMI7tl3N/+dgdf/epS\nXn21ltJSHx0dKQ4dinR97/M5CARcbNvWnFV7hw938NLfdlJYnEfp+PKurZC5iMfTOHQFdA6ESsGE\nSguHGeHlF/awYd0RTNOiekw+1984DU8oxO46B8lU/+0KIURfxhYn+OQX+w+gu/vRj9Zx550z+d73\n1rB8+WieemoPsZjR9f20aUVs3txEa2siq/ZefvkQ11xzEJ+pE/CEcu4/gG33HPddTphYaZKMtPHk\nY9vZt7cVTdOYOauM5ZdNxHYF2H1Yk10/QohBC7miPPzLzVmVTaVMHnlkB1dfPY5Vq/awcuUUfvzj\n9T3K3HzzJH7xi82YZnbB8EM/WssPJoSo8LpwO3PuPgD2SYF3wK8YW5ykpaGFR3+xlaNHY7jdOhcv\nH8u8hdWEU3kcPDq4ZwlxukkQPUguq4MN63P7L3vv3jDjx4e49toafvOb7T2+u+WWSfzxj+/k1N7D\nD2/jAx+YwR8e2UpJiS+rpGLdlZcHuu6Mdugwa1yC737zRdaf9HNt397Cs8/sZdQoP9/41hUcaMun\nPSpnWoQQuXE4oOFICx0dAyfxOq69PYXH48Dl0lm+fDRf+9prPb6/6aaJfPe7b+fUj8ce20VhoZdk\nrAylyDlJmNvjhGMvE/N8UFMc4Uuf/0uPc9gAW7c28Ztfb2XGjBK+8OVL2bTfQ9rI0KAQQvSjKASr\nX96bU501a+q5//6lPPXUXoqKvDQ09LxjavLkwl5z0YFs2dLEM8/s40MfmpFTveNcnhNhR2mBjStZ\nz6f+8YUeO4uAY/mA3uba6ybw3jsWsnHvIKN2IU4j2c49CG4X7N2dfRKv41at2sPll48lEHD12nZY\nXR1k167WnNpra0vi8zl49tl93HjjhJz7M3FKGYljQfSs8Uk+/+lVvQLo7urro9x15xOMDrXicctW\nGyFEbipKNJ58IrdJG8Cbbx5hzpxSUikzY8Cb606c4+2tXl3LihXVOdXNy3MSLOxcwXa7YHxRO3d9\n+E+9AujuNm9u5DOfeIKZYxNyNEYIkbPyAoM/PJJb7hvozH8zY0ZJjyS1AEVFXurq+h6z+vLUU3u5\n7LIxNDfHKSvLLdP23LmjSOEHID8AqqOOf/70s70C6O6eXLWbBx94nuljs3/xKsSZIivRg+B2Keqa\n+j9H53br3HTTRKZOLSKRMLBt0HXFkiUVtLX13naY69mS40pLfSxdWsm8eWVomsq6nXnzRuHydybG\nqS6D//nZmxypG/hsYDptcc9nn+U7P7qFzfvlzaAQInsOZdHS3P+55YICD7feOpnSUj+plIlSnYnD\nZs8uzXjcZbBjZ01NPq++WsvKlVN5/vmDWdd7/wdm0BDxABYTKwzu/cyzWQXxjY1xfvJvq7ntzkvZ\nd0QiaSFE9nTN6jfYhM7FmFtvnYzH4yCd7jw7Mnp0gGuuGcf3v98zgWIo5Mr6CEx3ra0JLrusmp//\nfDN33jmL//f/Xs+67ofvnMneY2NfdWGMO//huazqrVtbz+a1e8gfO5WwZPYW5xAJogchbYDf7+7z\n+wsvrOSaa8bz619v43e/29HjO5dLZ+XKKXz96xfxzW++0eNs32DU10c5cqSDVMrkE5+Yww9/uG7A\nOl6vg5Urp2Cl4oCXkCvK3/66L+tntrQkCDe1SGZvIUROLDR8vr5/7dx++1QqKwP87/9u67VKEgq5\nufPOmXz60/P5t39bc8r3NL/zTivjx4cIhxNcffW4rmsI+1NVFaB8lJ+ivBQNLQ6ibW0cPRobsN5x\nq1fX8qG7okDeKfRcCHG+sW2Fy6VnTACmaYp//ucFtLQk+Pd/39gr2B49OsCdd87E53Pyhz/sBDqT\nj/U3FvfF53Py2muHede7xpFOW0yeXMjOnS0D1rvwwkpaWuKUl0F7TLF1Y23WZ7EB/utn6/nBT8ez\nWTJ7i3OIbOcehHjCZsLkkozfXXhhJXPmlPIv//JKxlWTVMrkl7/cyg9/uI777luKy9V+hEE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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a8c9cfd3c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(17, 5))\n",
    "for sp, name, model in classifiers:\n",
    "    plt.subplot(1, 3, sp)\n",
    "    plot_decision_boundary(model, X_test, y_test)\n",
    "    plt.title(name)\n",
    "    plt.axis('off')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, the differences between the three classifiers become clearer. We see the single tree\n",
    "drawing by far the simplest decision boundaries, splitting the landscape using horizontal\n",
    "decision boundaries. The random forest is able to more clearly separate the cloud of data\n",
    "points in the lower left of the decision landscape. However, only extremely randomized\n",
    "trees were able to corner the cloud of data points towards the center of the landscape from\n",
    "all sides.\n",
    "\n",
    "Now that we know about all the different variations of tree ensembles, let's move on to a\n",
    "real-world dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "<!--NAVIGATION-->\n",
    "< [Understanding Ensemble Methods](10.01-Understanding-Ensemble-Methods.ipynb) | [Contents](../README.md) | [Using Random Forests for Face Recognition](10.03-Using-Random-Forests-for-Face-Recognition.ipynb) >"
   ]
  }
 ],
 "metadata": {
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   "language": "python",
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